Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review
Abstract
:1. Introduction
2. Methods
2.1. Search Strategy
2.2. Inclusion and Exclusion Criteria
2.3. Data Extraction
- Brand and company: The brand names of the EEG equipment and the company names that produce them.
- Type of EEG equipment: The articles that use either wired or wireless equipment. Nowadays, many applications provide wireless equipment because of its low cost and physical mobility [31].
- The sector of using EEG equipment: A broad area of applications that use BCIs as information sources [32].
3. Results
4. Discussion
5. Limitation
- Other digital libraries could have been used in the search for studies, which may impact candidate studies. However, we selected the largest libraries that deal with the SLR’s topic. Using other libraries may have just resulted in more duplicates.
- Herein, the number of selected studies can impact the conclusion drawn. The studies are pertaining to the medicine sector because of the keyword “rehab*”. However, we included two other keywords related to other sectors in the search string.
- This review mainly focused on English publications; however, there could be relevant publications in other languages.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Belkacem, A.N.; Jamil, N.; Palmer, J.A.; Ouhbi, S.; Chen, C. Brain computer interfaces for improving the quality of life of older adults and elderly patients. Front. Neurosci. 2020, 14, 692. [Google Scholar] [CrossRef]
- Pineda, J.A.; Allison, B.; Vankov, A. The effects of self-movement, observation, and imagination on/spl mu/rhythms and readiness potentials (RP’s): Toward a brain-computer interface (BCI). IEEE Trans. Rehabil. Eng. 2000, 8, 219–222. [Google Scholar] [CrossRef] [Green Version]
- Belkacem, A.N.; Nishio, S.; Suzuki, T.; Ishiguro, H.; Hirata, M. Neuromagnetic decoding of simultaneous bilateral hand movements for multidimensional brain–machine interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 1301–1310. [Google Scholar] [CrossRef]
- Zgallai, W.; Brown, J.T.; Ibrahim, A.; Mahmood, F.; Mohammad, K.; Khalfan, M.; Mohammed, M.; Salem, M.; Hamood, N. Deep learning AI application to an EEG driven BCI smart wheelchair. In Proceedings of the 2019 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 26 March–10 April 2019; pp. 1–5. [Google Scholar]
- Shao, L.; Zhang, L.; Belkacem, A.N.; Zhang, Y.; Chen, X.; Li, J.; Liu, H. EEG-controlled wall-crawling cleaning robot using SSVEP-based brain-computer interface. J. Healthc. Eng. 2020, 2020, 6968713. [Google Scholar] [CrossRef] [Green Version]
- Wolpaw, J.; Wolpaw, E.W. Brain-Computer Interfaces: Principles and Practice; Oxford University Press: New York, NY, USA, 2012. [Google Scholar]
- Gao, Q.; Dou, L.; Belkacem, A.N.; Chen, C. Noninvasive electroencephalogram based control of a robotic arm for writing task using hybrid BCI system. BioMed Res. Int. 2017, 2017, 8316485. [Google Scholar] [CrossRef] [Green Version]
- Fazel-Rezai, R.; Allison, B.Z.; Guger, C.; Sellers, E.W.; Kleih, S.C.; Kübler, A. P300 brain computer interface: Current challenges and emerging trends. Front. Neuroeng. 2012, 5, 14. [Google Scholar] [CrossRef] [Green Version]
- Sellers, E.W.; Vaughan, T.M.; Wolpaw, J.R. A brain-computer interface for long-term independent home use. Amyotroph. Lateral Scler. 2010, 11, 449–455. [Google Scholar] [CrossRef]
- Cincotti, F.; Mattia, D.; Aloise, F.; Bufalari, S.; Schalk, G.; Oriolo, G.; Cherubini, A.; Marciani, M.G.; Babiloni, F. Non-invasive brain–computer interface system: Towards its application as assistive technology. Brain Res. Bull. 2008, 75, 796–803. [Google Scholar] [CrossRef] [Green Version]
- Pires, G.; Torres, M.; Casaleiro, N.; Nunes, U.; Castelo-Branco, M. Playing Tetris with non-invasive BCI. In Proceedings of the 2011 IEEE 1st International Conference on Serious Games and Applications for Health (Segah), Braga, Portugal, 16–18 November 2011; pp. 1–6. [Google Scholar]
- Congedo, M.; Goyat, M.; Tarrin, N.; Ionescu, G.; Varnet, L.; Rivet, B.; Phlypo, R.; Jrad, N.; Acquadro, M.; Jutten, C. “Brain Invaders”: A prototype of an open-source P300-based video game working with the OpenViBE platform. In Proceedings of the 5th International Brain-Computer Interface Conference 2011 (BCI 2011), Graz, Austria, 22–24 September 2011; pp. 280–283. [Google Scholar]
- Kryger, M.; Wester, B.; Pohlmeyer, E.A.; Rich, M.; John, B.; Beaty, J.; McLoughlin, M.; Boninger, M.; Tyler-Kabara, E.C. Flight simulation using a Brain-Computer Interface: A pilot, pilot study. Exp. Neurol. 2017, 287, 473–478. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Prashant, P.; Joshi, A.; Gandhi, V. Brain computer interface: A review. In Proceedings of the 2015 5th Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, India, 26–28 November 2015; pp. 1–6. [Google Scholar]
- Kübler, A.; Neumann, N.; Kaiser, J.; Kotchoubey, B.; Hinterberger, T.; Birbaumer, N.P. Brain-computer communication: Self-regulation of slow cortical potentials for verbal communication. Arch. Phys. Med. Rehabil. 2001, 82, 1533–1539. [Google Scholar] [CrossRef]
- Zickler, C.; Halder, S.; Kleih, S.C.; Herbert, C.; Kübler, A. Brain painting: Usability testing according to the user-centered design in end users with severe motor paralysis. Artif. Intell. Med. 2013, 59, 99–110. [Google Scholar] [CrossRef] [PubMed]
- Birbaumer, N.; Ghanayim, N.; Hinterberger, T.; Iversen, I.; Kotchoubey, B.; Kübler, A.; Perelmouter, J.; Taub, E.; Flor, H. A spelling device for the paralysed. Nature 1999, 398, 297–298. [Google Scholar] [CrossRef] [PubMed]
- Mondal, D.D.; Alagirisamy, D.M. Brain Computer Interface (BCI): Mechanism and Challenges—A Survey. Int. J. Pharm. Res. 2020, 12, 2200–2208. [Google Scholar]
- Wilson, J.A.; Felton, E.A.; Garell, P.C.; Schalk, G.; Williams, J.C. ECoG factors underlying multimodal control of a brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2006, 14, 246–250. [Google Scholar] [CrossRef]
- Rezeika, A.; Benda, M.; Stawicki, P.; Gembler, F.; Saboor, A.; Volosyak, I. Brain–computer interface spellers: A review. Brain Sci. 2018, 8, 57. [Google Scholar] [CrossRef] [Green Version]
- Hwang, H.J.; Kwon, K.; Im, C.H. Neurofeedback-based motor imagery training for brain–computer interface (BCI). J. Neurosci. Methods 2009, 179, 150–156. [Google Scholar] [CrossRef]
- Lum, P.S.; Burgar, C.G.; Shor, P.C.; Majmundar, M.; Van der Loos, M. Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Arch. Phys. Med. Rehabil. 2002, 83, 952–959. [Google Scholar] [CrossRef] [Green Version]
- Remsik, A.; Young, B.; Vermilyea, R.; Kiekhoefer, L.; Abrams, J.; Evander Elmore, S.; Schultz, P.; Nair, V.; Edwards, D.; Williams, J.; et al. A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke. Expert Rev. Med Devices 2016, 13, 445–454. [Google Scholar] [CrossRef] [Green Version]
- Ramos-Murguialday, A.; Broetz, D.; Rea, M.; Läer, L.; Yilmaz, Ö.; Brasil, F.L.; Liberati, G.; Curado, M.R.; Garcia-Cossio, E.; Vyziotis, A.; et al. Brain–machine interface in chronic stroke rehabilitation: A controlled study. Ann. Neurol. 2013, 74, 100–108. [Google Scholar] [CrossRef] [PubMed]
- Henderson, S.; Skelton, H.; Rosenbaum, P. Assistive devices for children with functional impairments: Impact on child and caregiver function. Dev. Med. Child Neurol. 2008, 50, 89–98. [Google Scholar] [CrossRef]
- Demers, L.; Fuhrer, M.J.; Jutai, J.; Lenker, J.; Depa, M.; De Ruyter, F. A conceptual framework of outcomes for caregivers of assistive technology users. Am. J. Phys. Med. Rehabil. 2009, 88, 645–655. [Google Scholar] [CrossRef] [PubMed]
- Shute, V.J.; Zapata-Rivera, D. Adaptive technologies. ETS Res. Rep. Ser. 2007, 2007, i-34. [Google Scholar] [CrossRef]
- Zhang, L. A web-based English teaching module on the administration of electroencephalography-based neurofeedback training for Chinese students. NeuroQuantology 2018, 16. [Google Scholar] [CrossRef] [Green Version]
- Chiang, H.S.; Hsiao, K.L.; Liu, L.C. EEG-based detection model for evaluating and improving learning attention. J. Med. Biol. Eng. 2018, 38, 847–856. [Google Scholar] [CrossRef]
- Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. J. Clin. Epidemiol. 2009, 62, e1–e34. [Google Scholar] [CrossRef] [Green Version]
- Cypher, D.; Chevrollier, N.; Montavont, N.; Golmie, N. Prevailing over wires in healthcare environments: Benefits and challenges. IEEE Commun. Mag. 2006, 44, 56–63. [Google Scholar] [CrossRef] [Green Version]
- Horizon, B. Roadmap-The Future in Brain/Neural-Computer Interaction. Horizon 2020, 10, 978–983. [Google Scholar]
- Babu, G.M.; Balaji, S.V.; Adalarasu, K.; Nagasai, V.; Siva, A.; Geethanjali, B. Brain computer interface for vehicle navigation. Biomed. Res. 2017, 28, 344–350. [Google Scholar]
- Chen, C.; Zhou, P.; Belkacem, A.N.; Lu, L.; Xu, R.; Wang, X.; Tan, W.; Qiao, Z.; Li, P.; Gao, Q.; et al. Quadcopter Robot Control Based on Hybrid Brain–Computer Interface System. Sens. Mater. 2020, 32, 991–1004. [Google Scholar] [CrossRef] [Green Version]
- Belkacem, A.N.; Lakas, A. A case study on teaching a brain–computer interface interdisciplinary course to undergraduates. In Smart Education and e-Learning 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 215–228. [Google Scholar]
- Lekova, A.; Dimitrova, M.; Kostova, S.; Bouattane, O.; Ozaeta, L. Bci for assessing the emotional and cognitive skills of children with special educational needs. In Proceedings of the 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), Marrakech, Morocco, 21–27 October 2018; pp. 400–403. [Google Scholar]
- Kanagasabai, P.S.; Gautam, R.; Rathna, G. Brain-computer interface learning system for Quadriplegics. In Proceedings of the 2016 IEEE 4th International Conference on MOOCs, Innovation and Technology in Education (MITE), Madurai, India, 9–10 December 2016; pp. 258–262. [Google Scholar]
- Arnaldo, R.M.; Iglesias, J.; Gómez, V.F.; Crespo, J.; Pérez, L.; Alonso, J.F.; Sanz, A.R. Computerized brain interfaces for adaptive learning and assessment. In International Conference on Intelligent Human Systems Integration; Springer: Berlin/Heidelberg, Germany, 2018; pp. 237–241. [Google Scholar]
- Arana-Llanes, J.Y.; González-Serna, G.; Pineda-Tapia, R.; Olivares-Peregrino, V.; Ricarte-Trives, J.J.; Latorre-Postigo, J.M. EEG lecture on recommended activities for the induction of attention and concentration mental states on e-learning students. J. Intell. Fuzzy Syst. 2018, 34, 3359–3371. [Google Scholar] [CrossRef]
- Zhou, Y.; Xu, T.; Cai, Y.; Wu, X.; Dong, B. Monitoring cognitive workload in online videos learning through an EEG-based brain-computer interface. In International Conference on Learning and Collaboration Technologies; Springer: Berlin/Heidelberg, Germany, 2017; pp. 64–73. [Google Scholar]
- Crawford, C.S.; Gilbert, J.E. Neuroblock: A block-based programming approach to neurofeedback application development. In Proceedings of the 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Raleigh, NC, USA, 11–14 October 2017; pp. 303–307. [Google Scholar]
- Papakostas, M.; Tsiakas, K.; Giannakopoulos, T.; Makedon, F. Towards predicting task performance from EEG signals. In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 11–14 December 2017; pp. 4423–4425. [Google Scholar]
- Mehul, A.; Cioli, N.; Crawford, C.S.; Denham, A. Position: A novice oriented dual-modality programming tool for brain-computer interfaces application development. In Proceedings of the 2019 IEEE Blocks and Beyond Workshop (B&B), Memphis, TN, USA, 18 October 2019; pp. 27–30. [Google Scholar]
- Rizvi, S.T.H.; Karim, M.; Gulzar, M.M.; Javed, M.Y.; Furqan, M. Implementation of electroencephalography controlled prosthetic hand. Int. J. Syst. Control Commun. 2018, 9, 136–147. [Google Scholar] [CrossRef]
- Kim, S.; Kim, J. A novel semantically congruent audiovisual interface for assisting Brain-Machine Interface (BMI) performance enhancement. In International Conference on Human-Computer Interaction; Springer: Berlin/Heidelberg, Germany, 2019; pp. 165–171. [Google Scholar]
- Abiri, R.; Borhani, S.; Kilmarx, J.; Esterwood, C.; Jiang, Y.; Zhao, X. A Usability Study of Low-Cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model. IEEE Trans. Hum. Mach. Syst. 2020, 50, 287–297. [Google Scholar] [CrossRef] [PubMed]
- Yu, Y.C.; Garrison, H.; Battison, A.; Gabel, L. Control of a quadcopter with hybrid brain-computer interface. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 6–9 October 2019; pp. 2779–2784. [Google Scholar]
- Karunarathne, K.; Ekanayake, H. Controlling home appliances through thought commands. In Proceedings of the 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka, 26–29 September 2018; pp. 309–315. [Google Scholar]
- Yin, G.; Gong, L. Direction control and speed control combined model of motor-imagery based brain-actuated vehicle. In Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China, 26–28 July 2017; pp. 2210–2214. [Google Scholar]
- Chiuzbaian, A.; Jakobsen, J.; Puthusserypady, S. Mind controlled drone: An innovative multiclass SSVEP based brain computer interface. In Proceedings of the 2019 7th International Winter Conference on Brain-Computer Interface (BCI), Gangwon, Korea, 18–20 February 2019; pp. 1–5. [Google Scholar]
- Mezzina, G.; De Venuto, D. Smart sensors HW/SW interface based on brain-actuated personal care robot for ambient assisted living. In Proceedings of the 2020 IEEE Sensors, Rotterdam, The Netherlands, 25–28 October 2020; pp. 1–4. [Google Scholar]
- Aricò, P.; Borghini, G.; Di Flumeri, G.; Colosimo, A.; Bonelli, S.; Golfetti, A.; Pozzi, S.; Imbert, J.P.; Granger, G.; Benhacene, R.; et al. Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment. Front. Hum. Neurosci. 2016, 10, 539. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Penaloza, C.I.; Alimardani, M.; Nishio, S. Android feedback-based training modulates sensorimotor rhythms during motor imagery. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 666–674. [Google Scholar] [CrossRef]
- Stawicki, P.; Gembler, F.; Volosyak, I. Driving a semiautonomous mobile robotic car controlled by an SSVEP-based BCI. Comput. Intell. Neurosci. 2016, 2016, 4909685. [Google Scholar] [CrossRef] [Green Version]
- Al-Nuaimi, F.A.; Al-Nuaimi, R.J.; Al-Dhaheri, S.S.; Ouhbi, S.; Belkacem, A.N. Mind drone chasing using EEG-based brain computer interface. In Proceedings of the 2020 16th International Conference on Intelligent Environments (IE), Madrid, Spain, 20–23 July 2020; pp. 74–79. [Google Scholar]
- Kangassalo, L.; Spapé, M.; Ruotsalo, T. Neuroadaptive modelling for generating images matching perceptual categories. Sci. Rep. 2020, 10, 1–10. [Google Scholar] [CrossRef]
- Wenzel, M.A.; Bogojeski, M.; Blankertz, B. Real-time inference of word relevance from electroencephalogram and eye gaze. J. Neural Eng. 2017, 14, 056007. [Google Scholar] [CrossRef] [Green Version]
- Jeong, J.H.; Shim, K.H.; Kim, D.J.; Lee, S.W. Trajectory decoding of arm reaching movement imageries for brain-controlled robot arm system. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 5544–5547. [Google Scholar]
- Zhao, W.; Zhang, X.; Qu, J.; Xiao, J.; Huang, Y. A virtual smart home based on EEG control. In Proceedings of the 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 12–14 July 2019; pp. 85–89. [Google Scholar]
- Ko, L.W.; Chang, Y.; Wu, P.L.; Tzou, H.A.; Chen, S.F.; Tang, S.C.; Yeh, C.L.; Chen, Y.J. Development of a smart helmet for strategical BCI applications. Sensors 2019, 19, 1867. [Google Scholar] [CrossRef] [Green Version]
- Rashid, M.; Sulaiman, N.; Mustafa, M.; Khatun, S.; Bari, B.S.; Hasan, M.J.; Al-Fakih, N.M. Investigating the possibility of brain actuated mobile robot through single-channel EEG headset. In InECCE2019; Springer: Berlin/Heidelberg, Germany, 2020; pp. 579–590. [Google Scholar]
- Lin, W.J.; Chiu, M.C. Design a personalized brain-computer interface of legorobot assisted by data analysis method. In Transdisciplinary Engineering: A Paradigm Shift; IOS Press: Amsterdam, The Netherlands, 2017; p. 311. [Google Scholar]
- Yang, D.; Nguyen, T.H.; Chung, W.Y. A bipolar-channel hybrid brain-computer interface system for home automation control utilizing steady-state visually evoked potential and eye-blink signals. Sensors 2020, 20, 5474. [Google Scholar] [CrossRef]
- Augustian, M.; ur Réhman, S.; Sandvig, A.; Kotikawatte, T.; Yongcui, M.; Evensmoen, H.R. EEG analysis from motor imagery to control a forestry crane. In International Conference on Intelligent Human Systems Integration; Springer: Berlin/Heidelberg, Germany, 2018; pp. 281–286. [Google Scholar]
- Shantala, C.; Rashmi, C. Mind controlled wireless robotic arm using brain computer interface. In Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 14–16 December 2017; pp. 1–8. [Google Scholar]
- Akinola, I.; Chen, B.; Koss, J.; Patankar, A.; Varley, J.; Allen, P. Task level hierarchical system for BCI-enabled shared autonomy. In Proceedings of the 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), Birmingham, UK, 15–17 November 2017; pp. 219–225. [Google Scholar]
- Wang, J.; Wang, W.; Hou, Z.G.; Shi, W.; Liang, X.; Ren, S.; Peng, L.; Zhou, Y.J. BCI and multimodal feedback based attention regulation for lower limb rehabilitation. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019; pp. 1–7. [Google Scholar]
- Wang, J.; Wang, W.; Hou, Z.G. Toward Improving Engagement in Neural Rehabilitation: Attention Enhancement Based on Brain–Computer Interface and Audiovisual Feedback. IEEE Trans. Cogn. Dev. Syst. 2019, 12, 787–796. [Google Scholar] [CrossRef]
- Zhao, C.; Zhang, Z.; Li, Y.; Pan, X.; Qu, J.; Yan, Z. An EEG-based mind controlled virtual-human obstacle-avoidance platform in three dimensional virtual environment. In Proceedings of the 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), Shanghai, China, 25–28 May 2017; pp. 387–390. [Google Scholar]
- Vega, I.; Adarve, C.; Villar-Vega, H.; Páramo, C. Serious game for real-time brain-computer interface training. J. Phys. Conf. Ser. 2019, 1418, 012011. [Google Scholar] [CrossRef]
- Carofiglio, V.; De Carolis, B.; D’Errico, F. A BCI-based Assessment of a Player’s State of Mind for Game Adaptation. Available online: http://ceur-ws.org/Vol-2480/GHItaly19_paper_04.pdf (accessed on 11 July 2021).
- Langroudi, G.; Jordanous, A.; Li, L. Music emotion capture: Sonifying emotions in EEG data. In Proceedings of the Symposium on Emotion Modelling and Detection in Social Media and Online Interaction, Liverpool, UK, 4–6 April 2018. [Google Scholar]
- McMahon, M.; Schukat, M. A low-cost, open-source, BCI-VR prototype for real-time signal processing of EEG to manipulate 3D VR objects as a form of neurofeedback. In Proceedings of the 2018 29th Irish Signals and Systems Conference (ISSC), Belfast, UK, 21–22 June 2018; pp. 1–6. [Google Scholar]
- McMahon, M.; Schukat, M. A low-cost, open-source, BCI-VR game control development environment prototype for game based neurorehabilitation. In Proceedings of the 2018 IEEE Games, Entertainment, Media Conference (GEM), Galway, Ireland, 15–17 August 2018; pp. 1–9. [Google Scholar]
- Arvaneh, M.; Robertson, I.H.; Ward, T.E. A p300-based brain-computer interface for improving attention. Front. Hum. Neurosci. 2019, 12, 524. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Leite, H.M.d.A.; Carvalho, S.N.d.; Costa, T.B.d.S.; Attux, R.; Hornung, H.H.; Arantes, D.S. Analysis of user interaction with a brain-computer interface based on steady-state visually evoked potentials: Case study of a game. Comput. Intell. Neurosci. 2018, 2018, 4920132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cunha, J.D.; Scherer, R. Are online co-adaptive sensorimotor rhythm brain-computer interface training paradigms effective? In Proceedings of the 2018 International Conference on Cyberworlds (CW), Singapore, 3–5 October 2018; pp. 419–422. [Google Scholar]
- Labonte-Lemoyne, E.; Courtemanche, F.; Louis, V.; Fredette, M.; Sénécal, S.; Léger, P.M. Dynamic threshold selection for a biocybernetic loop in an adaptive video game context. Front. Hum. Neurosci. 2018, 12, 282. [Google Scholar] [CrossRef]
- Myrden, A.; Chau, T. Towards psychologically adaptive brain–computer interfaces. J. Neural Eng. 2016, 13, 066022. [Google Scholar] [CrossRef]
- Adama, V.; Hoffman, J.; Bogdan, M. Coupling Brain-Computer Interface and Electrical Stimulation for Stroke Rehabilitation and Tremor Reduction in Parkinson’s Disease. In Proceedings of the The International Workshop on Innovative Simulation for Healthcare, Larnaca, Cyprus, 26–28 September 2016; pp. 51–57. [Google Scholar]
- Schwarz, A.; Höller, M.K.; Pereira, J.; Ofner, P.; Müller-Putz, G.R. Decoding hand movements from human EEG to control a robotic arm in a simulation environment. J. Neural Eng. 2020, 17, 036010. [Google Scholar] [CrossRef]
- Ibáñez, J.; Monge-Pereira, E.; Molina-Rueda, F.; Serrano, J.; Del Castillo, M.D.; Cuesta-Gómez, A.; Carratalá-Tejada, M.; Cano-de-la Cuerda, R.; Alguacil-Diego, I.M.; Miangolarra-Page, J.C.; et al. Low latency estimation of motor intentions to assist reaching movements along multiple sessions in chronic stroke patients: A feasibility study. Front. Neurosci. 2017, 11, 126. [Google Scholar] [CrossRef]
- Randazzo, L.; Iturrate, I.; Perdikis, S.; Millán, J.d.R. mano: A wearable hand exoskeleton for activities of daily living and neurorehabilitation. IEEE Robot. Autom. Lett. 2017, 3, 500–507. [Google Scholar] [CrossRef] [Green Version]
- Liang, H.; Zhu, C.; Iwata, Y.; Maedono, S.; Mochida, M.; Yu, H.; Yan, Y.; Duan, F. Motion estimation for the control of upper limb wearable exoskeleton robot with electroencephalography signals. In Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), Shenzhen, China, 25–27 October 2018; pp. 228–233. [Google Scholar]
- Mallikarachchi, S.; Chinthaka, D.; Sandaruwan, J.; Ruhunage, I.; Lalitharatne, T.D. Motor imagery EEG-EOG signals based Brain Machine Interface (BMI) for a Mobile Robotic Assistant (MRA). In Proceedings of the 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece, 28–30 October 2019; pp. 812–816. [Google Scholar]
- Vourvopoulos, A.; i Badia, S.B. Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: A within-subject analysis. J. Neuroeng. Rehabil. 2016, 13, 1–14. [Google Scholar] [CrossRef] [Green Version]
- Gomez-Pilar, J.; Corralejo, R.; Nicolas-Alonso, L.F.; Álvarez, D.; Hornero, R. Neurofeedback training with a motor imagery-based BCI: Neurocognitive improvements and EEG changes in the elderly. Med Biol. Eng. Comput. 2016, 54, 1655–1666. [Google Scholar] [CrossRef] [Green Version]
- Lim, H.; Ku, J. A brain–computer interface-based action observation game that enhances mu suppression. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 2290–2296. [Google Scholar] [CrossRef]
- Liu, D.; Chen, W.; Pei, Z.; Wang, J. A brain-controlled lower-limb exoskeleton for human gait training. Rev. Sci. Instruments 2017, 88, 104302. [Google Scholar] [CrossRef]
- Amaral, C.; Mouga, S.; Simões, M.; Pereira, H.C.; Bernardino, I.; Quental, H.; Playle, R.; McNamara, R.; Oliveira, G.; Castelo-Branco, M. A feasibility clinical trial to improve social attention in Autistic Spectrum Disorder (ASD) using a brain computer interface. Front. Neurosci. 2018, 12, 477. [Google Scholar] [CrossRef] [Green Version]
- Käthner, I.; Halder, S.; Hintermüller, C.; Espinosa, A.; Guger, C.; Miralles, F.; Vargiu, E.; Dauwalder, S.; Rafael-Palou, X.; Solà, M.; et al. A multifunctional brain-computer interface intended for home use: An evaluation with healthy participants and potential end users with dry and gel-based electrodes. Front. Neurosci. 2017, 11, 286. [Google Scholar] [CrossRef] [Green Version]
- Hashimoto, Y.; Kakui, T.; Ushiba, J.; Liu, M.; Kamada, K.; Ota, T. Development of rehabilitation system with brain-computer interface for subacute stroke patients. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 7–10 October 2018; pp. 51–56. [Google Scholar]
- Kawakami, M.; Fujiwara, T.; Ushiba, J.; Nishimoto, A.; Abe, K.; Honaga, K.; Nishimura, A.; Mizuno, K.; Kodama, M.; Masakado, Y.; et al. A new therapeutic application of brain-machine interface (BMI) training followed by hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy for patients with severe hemiparetic stroke: A proof of concept study. Restor. Neurol. Neurosci. 2016, 34, 789–797. [Google Scholar] [CrossRef]
- Will, M.; Peter, T.; Hanses, M.; Elkmann, N.; Rose, G.; Hinrichs, H.; Reichert, C. A robot control platform for motor impaired people. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 2025–2030. [Google Scholar]
- Chowdhury, A.; Meena, Y.K.; Raza, H.; Bhushan, B.; Uttam, A.K.; Pandey, N.; Hashmi, A.A.; Bajpai, A.; Dutta, A.; Prasad, G. Active physical practice followed by mental practice using BCI-driven hand exoskeleton: A pilot trial for clinical effectiveness and usability. IEEE J. Biomed. Health Inform. 2018, 22, 1786–1795. [Google Scholar] [CrossRef]
- Aliakbaryhosseinabadi, S.; Mrachacz-Kersting, N. Adaptive brain-computer interface with attention alterations in patients with amyotrophic lateral sclerosis. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020; pp. 3188–3191. [Google Scholar]
- Nierula, B.; Spanlang, B.; Martini, M.; Borrell, M.; Nikulin, V.V.; Sanchez-Vives, M.V. Agency and responsibility over virtual movements controlled through different paradigms of brain- computer interface. J. Physiol. 2019, 599, 2419–2434. [Google Scholar] [CrossRef]
- Martínez-Cagigal, V.; Gomez-Pilar, J.; Alvarez, D.; Hornero, R. An asynchronous P300-based brain-computer interface web browser for severely disabled people. IEEE Trans. Neural Syst. Rehabil. Eng. 2016, 25, 1332–1342. [Google Scholar] [CrossRef]
- Shahriari, Y.; Vaughan, T.M.; McCane, L.; Allison, B.Z.; Wolpaw, J.R.; Krusienski, D.J. An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data. J. Neural Eng. 2019, 16, 056031. [Google Scholar] [CrossRef]
- Hernández-Rojas, L.G.; Montoya, O.M.; Antelis, J.M. Anticipatory detection of self-paced rehabilitative movements in the same upper limb from EEG signals. IEEE Access 2020, 8, 119728–119743. [Google Scholar] [CrossRef]
- Sánchez, B.C.C.; Carvajal, L.C.L.; Quitian, F.L.G.T.; López, J.M.L. BCI for meal assistance device. In Latin American Conference on Biomedical Engineering; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1117–1121. [Google Scholar]
- McFarland, D.; Norman, S.; Sarnacki, W.; Wolbrecht, E.; Reinkensmeyer, D.; Wolpaw, J. BCI-based sensorimotor rhythm training can affect individuated finger movements. Brain-Comput. Interfaces 2020, 7, 38–46. [Google Scholar] [CrossRef]
- Mattia, D.; Pichiorri, F.; Colamarino, E.; Masciullo, M.; Morone, G.; Toppi, J.; Pisotta, I.; Tamburella, F.; Lorusso, M.; Paolucci, S.; et al. The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: A study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response. BMC Neurol. 2020, 20, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Karácsony, T.; Hansen, J.P.; Iversen, H.K.; Puthusserypady, S. Brain computer interface for neuro-rehabilitation with deep learning classification and virtual reality feedback. In Proceedings of the 10th Augmented Human International Conference 2019, New York, NY, USA, 11–12 March 2019; pp. 1–8. [Google Scholar]
- Di Flumeri, G.; De Crescenzio, F.; Berberian, B.; Ohneiser, O.; Kramer, J.; Aricò, P.; Borghini, G.; Babiloni, F.; Bagassi, S.; Piastra, S. Brain–computer interface-based adaptive automation to prevent out-of-the-loop phenomenon in air traffic controllers dealing with highly automated systems. Front. Hum. Neurosci. 2019, 13, 296. [Google Scholar] [CrossRef] [Green Version]
- Irimia, D.C.; Cho, W.; Ortner, R.; Allison, B.Z.; Ignat, B.E.; Edlinger, G.; Guger, C. Brain-computer interfaces with multi-sensory feedback for stroke rehabilitation: A case study. Artif. Organs 2017, 41, E178–E184. [Google Scholar] [CrossRef]
- Schildt, C.J.; Thomas, S.H.; Powell, E.S.; Sawaki, L.; Sunderam, S. Closed-loop afferent electrical stimulation for recovery of hand function in individuals with motor incomplete spinal injury: Early clinical results. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; pp. 1552–1555. [Google Scholar]
- Resquín, F.; Ibáñez, J.; Gonzalez-Vargas, J.; Brunetti, F.; Dimbwadyo, I.; Alves, S.; Carrasco, L.; Torres, L.; Pons, J.L. Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; pp. 6381–6384. [Google Scholar]
- Suwannarat, A.; Pan-Ngum, S.; Israsena, P. Comparison of EEG measurement of upper limb movement in motor imagery training system. Biomed. Eng. Online 2018, 17, 1–22. [Google Scholar] [CrossRef] [Green Version]
- Liang, H.; Zhu, C.; Tian, Y.; Iwata, Y.; Maedono, S.; Yu, H.; Yan, Y.; Duan, F. Construction of power assistive system for the control of upper limb wearable exoskeleton robot with electroencephalography signals. In Proceedings of the 2017 IEEE International Conference on Cyborg and Bionic Systems (CBS), Beijing, China, 17–19 October 2017; pp. 165–168. [Google Scholar]
- Chowdhury, A.; Raza, H.; Dutta, A.; Prasad, G. EEG-EMG based hybrid brain computer interface for triggering hand exoskeleton for neuro-rehabilitation. In Proceedings of the Advances in Robotics, New Delhi, India, 28 June–2 July 2017; pp. 1–6. [Google Scholar]
- Scherer, R.; Schwarz, A.; Müller-Putz, G.R.; Pammer-Schindler, V.; Garcia, M.L. Game-based BCI training: Interactive design for individuals with cerebral palsy. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, 9–12 October 2015; pp. 3175–3180. [Google Scholar]
- Carrere, L.; Tabernig, C. Motor imagery BCI system with visual feedback: Design and preliminary evaluation. In Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, 26–28 October 2016; Springer: Berlin/Heidelberg, Germany, 2017; pp. 709–712. [Google Scholar]
- Martínez-Cagigal, V.; Santamaría-Vázquez, E.; Gomez-Pilar, J.; Hornero, R. Towards an accessible use of smartphone-based social networks through brain-computer interfaces. Expert Syst. Appl. 2019, 120, 155–166. [Google Scholar] [CrossRef]
- Kobayashi, N.; Sato, K. P300-based control for assistive robot for habitat. In Proceedings of the 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), Nagoya, Japan, 24–27 October 2017; pp. 1–5. [Google Scholar]
- Zhang, S.; Yoshida, W.; Mano, H.; Yanagisawa, T.; Mancini, F.; Shibata, K.; Kawato, M.; Seymour, B. Pain control by co-adaptive learning in a brain-machine interface. Curr. Biol. 2020, 30, 3935–3944. [Google Scholar] [CrossRef]
- Pacheco, K.; Acuna, K.; Carranza, E.; Achanccaray, D.; Andreu-Perez, J. Performance predictors of motor imagery brain-computer interface based on spatial abilities for upper limb rehabilitation. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea, 11–15 July 2017; pp. 1014–1017. [Google Scholar]
- Hashimoto, Y.; Kakui, T.; Ushiba, J.; Liu, M.; Kamada, K.; Ota, T. Portable rehabilitation system with brain-computer interface for inpatients with acute and subacute stroke: A feasibility study. Assist. Technol. 2020. [Google Scholar] [CrossRef]
- Elvira, M.; Iáñez, E.; Quiles, V.; Ortiz, M.; Azorín, J.M. Pseudo-online BMI based on EEG to detect the appearance of sudden obstacles during walking. Sensors 2019, 19, 5444. [Google Scholar] [CrossRef] [Green Version]
- Aliakbaryhosseinabadi, S.; Farina, D.; Mrachacz-Kersting, N. Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis. J. Neural Eng. 2020, 17, 036017. [Google Scholar] [CrossRef]
- Osuagwu, B.C.; Wallace, L.; Fraser, M.; Vuckovic, A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: A randomised pilot study. J. Neural Eng. 2016, 13, 065002. [Google Scholar] [CrossRef]
- Alimardani, M.; Kemmeren, L.; Okumura, K.; Hiraki, K. Robot-assisted mindfulness practice: Analysis of neurophysiological responses and affective state change. In Proceedings of the 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy, 31 August–4 September 2020; pp. 683–689. [Google Scholar]
- Mezzina, G.; De Venuto, D. Semi-autonomous personal care robots interface driven by EEG signals digitization. In Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France, 9–13 March 2020; pp. 264–269. [Google Scholar]
- Jebri, A.; Madani, T.; Djouani, K. Neural Adaptive Integral-Sliding-Mode Controller with a SSVEP-based BCI for Exoskeletons. In Proceedings of the 2019 19th International Conference on Advanced Robotics (ICAR), Belo Horizonte, Brazil, 2–6 December 2019; pp. 87–92. [Google Scholar]
- Yu, G.; Wang, J.; Chen, W.; Zhang, J. EEG-based brain-controlled lower extremity exoskeleton rehabilitation robot. In Proceedings of the 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Ningbo, China, 19–21 November 2017; pp. 763–767. [Google Scholar]
- Xu, R.; Dosen, S.; Jiang, N.; Yao, L.; Farooq, A.; Jochumsen, M.; Mrachacz-Kersting, N.; Dremstrup, K.; Farina, D. Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch. J. Neural Eng. 2019, 16, 056001. [Google Scholar] [CrossRef]
- Irimia, D.C.; Poboroniuc, M.S.; Serea, F.; Baciu, A.; Olaru, R. Controlling a FES-EXOSKELETON rehabilitation system by means of brain-computer interface. In Proceedings of the 2016 International Conference and Exposition on Electrical and Power Engineering (EPE), Iasi, Romania, 20–22 October 2016; pp. 352–355. [Google Scholar]
- Norman, S.; McFarland, D.; Miner, A.; Cramer, S.; Wolbrecht, E.; Wolpaw, J.; Reinkensmeyer, D. Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke. J. Neural Eng. 2018, 15, 056026. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nataraj, S.K.; Paulraj, M.; Yaacob, S.B.; Adom, A.H.B. Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine. J. Med. Signals Sens. 2020, 10, 228. [Google Scholar]
- Hortal, E.; Úbeda, A.; Iáñez, E.; Azorín, J.M.; Fernández, E. EEG-based detection of starting and stopping during gait cycle. Int. J. Neural Syst. 2016, 26, 1650029. [Google Scholar] [CrossRef]
- Marghi, Y.M.; Farjadian, A.B.; Yen, S.C.; Erdogmus, D. EEG-guided robotic mirror therapy system for lower limb rehabilitation. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea, 11–15 July 2017; pp. 1917–1921. [Google Scholar]
- Kumar, A.; Fang, Q.; Fu, J.; Pirogova, E.; Gu, X. Error-related neural responses recorded by electroencephalography during post-stroke rehabilitation movements. Front. Neurorobotics 2019, 13, 107. [Google Scholar] [CrossRef] [PubMed]
- Delijorge, J.; Mendoza-Montoya, O.; Gordillo, J.L.; Caraza, R.; Martinez, H.R.; Antelis, J.M. Evaluation of a P300-based brain-machine interface for a robotic hand-orthosis control. Front. Neurosci. 2020, 14, 589659. [Google Scholar] [CrossRef]
- Alchalabi, A.E.; Shirmohammadi, S.; Eddin, A.N.; Elsharnouby, M. FOCUS: Detecting ADHD patients by an EEG-based serious game. IEEE Trans. Instrum. Meas. 2018, 67, 1512–1520. [Google Scholar] [CrossRef]
- Braun, J.F.; Díez-Valencia, G.; Ehrlich, S.K.; Lanillos, P.; Cheng, G. A prototype of a P300 based brain-robot interface to enable multi-modal interaction for patients with limited mobility. In Proceedings of the 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS), Munich, Germany, 18–20 September 2019; pp. 78–84. [Google Scholar]
- Romo Badillo, M.; Toriz Palacios, A.; Nuño de la Parra, P. Brain-Computer Interface (BCI) Development for Motor Disabled People Integration in the Manufacturing SME. Comput. Y Sist. 2018, 22, 505–520. [Google Scholar] [CrossRef]
- Salisbury, D.B.; Parsons, T.D.; Monden, K.R.; Trost, Z.; Driver, S.J. Brain–computer interface for individuals after spinal cord injury. Rehabil. Psychol. 2016, 61, 435. [Google Scholar] [CrossRef] [PubMed]
- Vinoj, P.; Jacob, S.; Menon, V.G.; Rajesh, S.; Khosravi, M.R. Brain-controlled adaptive lower limb exoskeleton for rehabilitation of post-stroke paralyzed. IEEE Access 2019, 7, 132628–132648. [Google Scholar] [CrossRef]
- Barresi, G.; Olivieri, E.; Caldwell, D.G.; Mattos, L.S. Brain-controlled AR feedback design for user’s training in surgical HRI. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, 9–12 October 2015; pp. 1116–1121. [Google Scholar]
- Zeng, H.; Wang, Y.; Wu, C.; Song, A.; Liu, J.; Ji, P.; Xu, B.; Zhu, L.; Li, H.; Wen, P. Closed-loop hybrid gaze brain-machine interface based robotic arm control with augmented reality feedback. Front. Neurorobotics 2017, 11, 60. [Google Scholar] [CrossRef] [Green Version]
- Manolova, A.; Tsenov, G.; Lazarova, V.; Neshov, N. Combined EEG and EMG fatigue measurement framework with application to hybrid brain-computer interface. In Proceedings of the 2016 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Varna, Bulgaria, 6–9 June 2016; pp. 1–5. [Google Scholar]
- Athanasiou, A.; Arfaras, G.; Xygonakis, I.; Kartsidis, P.; Pandria, N.; Kavazidi, K.R.; Astaras, A.; Foroglou, N.; Polyzoidis, K.; Bamidis, P.D. Commercial BCI Control and functional brain networks in spinal cord injury: A proof-of-concept. In Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, Greece, 22–24 June 2017; pp. 262–267. [Google Scholar]
- Wang, Y.; Xu, G.; Song, A.; Xu, B.; Li, H.; Hu, C.; Zeng, H. Continuous shared control for robotic arm reaching driven by a hybrid gaze-brain machine interface. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October 2018; pp. 4462–4467. [Google Scholar]
- Naijian, C.; Xiangdong, H.; Yantao, W.; Xinglai, C.; Hui, C. Coordination control strategy between human vision and wheelchair manipulator based on BCI. In Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China, 5–7 June 2016; pp. 1872–1875. [Google Scholar]
- Yu, Y.C.; Smith, B.; Goreshnik, A.; Gabel, L. Design of an affordable brain-computer interface for robot navigation. In Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China, 19–21 June 2019; pp. 787–792. [Google Scholar]
- Huang, X.; Xue, X.; Yuan, Z. A simulation platform for the Brain-Computer Interface (BCI) based smart wheelchair. In International Conference on Artificial Intelligence and Security; Springer: Berlin/Heidelberg, Germany, 2020; pp. 257–266. [Google Scholar]
- Von Gwayneth, B.A.; Dungca, C.J.N.; Lazam, S.A.; Pereira, M.N.L.; Tan, J.R.O.; Prado, S.V. Development of an EEG-based motor imagery brain-computer interface system for lower limb assistive technologies. In Proceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, 29 November–2 December 2018; pp. 1–6. [Google Scholar]
- Bousseta, R.; El Ouakouak, I.; Gharbi, M.; Regragui, F. EEG based brain computer interface for controlling a robot arm movement through thought. Irbm 2018, 39, 129–135. [Google Scholar] [CrossRef]
- Osama, M.; Aslam, M.H. Emotiv EPOC+ fed electrical muscle stimulation system; an inexpensive brain-computer interface for rehabilitation of neuro-muscular disorders. JPMA 2020, 2019, 526–530. [Google Scholar]
- Taherian, S.; Selitskiy, D.; Pau, J.; Davies, T.C.; Owens, R.G. Training to use a commercial brain-computer interface as access technology: A case study. Disabil. Rehabil. Assist. Technol. 2016, 11, 345–350. [Google Scholar] [CrossRef]
- Bi, Q.; Yang, C.; Yang, W.; Fan, J.; Wang, H. Hand Exoskeleton Control for Cerebrum Plasticity Training Based on Brain–Computer Interface. In Wearable Sensors and Robots; Springer: Berlin/Heidelberg, Germany, 2017; pp. 395–410. [Google Scholar]
- Achic, F.; Montero, J.; Penaloza, C.; Cuellar, F. Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks. In Proceedings of the 2016 IEEE workshop on advanced robotics and its social impacts (ARSO), Shanghai, China, 8–10 July 2016; pp. 249–254. [Google Scholar]
- Alchalcabi, A.E.; Eddin, A.N.; Shirmohammadi, S. More attention, less deficit: Wearable EEG-based serious game for focus improvement. In Proceedings of the 2017 IEEE 5th International Conference on Serious Games and Applications for Health (SeGAH), Perth, Australia, 2–4 April 2017; pp. 1–8. [Google Scholar]
- Kumar, P.; Saini, R.; Sahu, P.K.; Roy, P.P.; Dogra, D.P.; Balasubramanian, R. Neuro-phone: An assistive framework to operate Smartphone using EEG signals. In Proceedings of the 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, India, 14–16 July 2017; pp. 1–5. [Google Scholar]
- Abiri, R.; Borhani, S.; Zhao, X.; Jiang, Y. Real-time brain machine interaction via social robot gesture control. In Proceedings of the Dynamic Systems and Control Conference, Tysons Corner, VA, USA, 11–13 October 2017. [Google Scholar]
- Chu, Y.; Zhao, X.; Zou, Y.; Xu, W.; Zhao, Y. Robot-assisted rehabilitation system based on SSVEP brain-computer interface for upper extremity. In Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia, 12–15 December 2018; pp. 1098–1103. [Google Scholar]
- Staffa, M.; Giordano, M.; Ficuciello, F. A WiSARD network approach for a BCI-based robotic prosthetic control. Int. J. Soc. Robot. 2020, 12, 749–764. [Google Scholar] [CrossRef]
- Schiatti, L.; Tessadori, J.; Barresi, G.; Mattos, L.S.; Ajoudani, A. Soft brain-machine interfaces for assistive robotics: A novel control approach. In Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017; pp. 863–869. [Google Scholar]
- Zhao, X.; Chu, Y.; Han, J.; Zhang, Z. SSVEP-based brain–computer interface controlled functional electrical stimulation system for upper extremity rehabilitation. IEEE Trans. Syst. Man, Cybern. Syst. 2016, 46, 947–956. [Google Scholar] [CrossRef]
- Li, Y.; Zhou, G.; Graham, D.; Holtzhauer, A. Towards an EEG-based brain-computer interface for online robot control. Multimed. Tools Appl. 2016, 75, 7999–8017. [Google Scholar] [CrossRef]
- Ihsan, I.A.; Tomari, R.; Zakaria, W.N.W.; Othman, N. Alternative input medium development for wheelchair user with severe spinal cord injury. AIP Conf. Proc. 2017, 1883, 020032. [Google Scholar]
- Ramirez, R. An Expressive Brain-Computer Music Interface for Musical Neurofeedback; University of Michigan Library: Ann Arbor, MI, USA, 2018. [Google Scholar]
- Ali, H.A.; Goga, N.; Marian, C.V.; Ali, L.A. An Investigation of Mind-Controlled Prosthetic Arm Intelligent System. ELearning Softw. Educ. 2020, 2, 17–26. [Google Scholar]
- Al-Hudhud, G.; Alqahtani, L.; Albaity, H.; Alsaeed, D.; Al-Turaiki, I. Analyzing passive BCI signals to control adaptive automation devices. Sensors 2019, 19, 3042. [Google Scholar] [CrossRef] [Green Version]
- Taherian, S.; Selitskiy, D.; Pau, J.; Claire Davies, T. Are we there yet? Evaluating commercial grade brain–computer interface for control of computer applications by individuals with cerebral palsy. Disabil. Rehabil. Assist. Technol. 2017, 12, 165–174. [Google Scholar] [CrossRef]
- Sreeja, S.; Joshi, V.; Samima, S.; Saha, A.; Rabha, J.; Cheema, B.S.; Samanta, D.; Mitra, P. BCI augmented text entry mechanism for people with special needs. In International Conference on Intelligent Human Computer Interaction; Springer: Berlin/Heidelberg, Germany, 2016; pp. 81–93. [Google Scholar]
- Qiu, S.; Li, Z.; He, W.; Zhang, L.; Yang, C.; Su, C.Y. Brain–machine interface and visual compressive sensing-based teleoperation control of an exoskeleton robot. IEEE Trans. Fuzzy Syst. 2016, 25, 58–69. [Google Scholar] [CrossRef] [Green Version]
- Khan, A.; Chen, C.; Yuan, K.; Wang, X.; Mehra, P.; Liu, Y.; Tong, K.Y. Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke. Top. Stroke Rehabil. 2020, 28, 276–288. [Google Scholar] [CrossRef] [PubMed]
- Li, Z.; He, W.; Yang, C.; Qiu, S.; Zhang, L.; Su, C.Y. Teleoperation control of an exoskeleton robot using brain machine interface and visual compressive sensing. In Proceedings of the 2016 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 12–15 June 2016; pp. 1550–1555. [Google Scholar]
- Chen, S.C.; Hsu, C.H.; Kuo, H.C.; Zaeni, I.A. The BCI control applied to the interactive autonomous robot with the function of meal assistance. In Proceedings of the 3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES2014); Springer: Berlin/Heidelberg, Germany, 2016; pp. 475–483. [Google Scholar]
- Xiao, J.; Pan, J.; He, Y.; Xie, Q.; Yu, T.; Huang, H.; Lv, W.; Zhang, J.; Yu, R.; Li, Y. Visual fixation assessment in patients with disorders of consciousness based on brain-computer interface. Neurosci. Bull. 2018, 34, 679–690. [Google Scholar] [CrossRef] [Green Version]
- Deng, X.; Yu, Z.L.; Lin, C.; Gu, Z.; Li, Y. A bayesian shared control approach for wheelchair robot with brain machine interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2019, 28, 328–338. [Google Scholar] [CrossRef] [PubMed]
- Xiao, J.; Lin, Q.; Yu, T.; Xie, Q.; Yu, R.; Li, Y. A BCI system for assisting visual fixation assessment in behavioral evaluation of patients with disorders of consciousness. In Proceedings of the 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), Shanghai, China, 25–28 May 2017; pp. 399–402. [Google Scholar]
- Wang, F.; He, Y.; Qu, J.; Cao, Y.; Liu, Y.; Li, F.; Yu, Z.; Yu, R.; Li, Y. A Brain–Computer Interface Based on Three-Dimensional Stereo Stimuli for Assisting Clinical Object Recognition Assessment in Patients With Disorders of Consciousness. IEEE Trans. Neural Syst. Rehabil. Eng. 2019, 27, 507–513. [Google Scholar] [CrossRef] [PubMed]
- Wang, K.; Wang, Z.; Guo, Y.; He, F.; Qi, H.; Xu, M.; Ming, D. A brain-computer interface driven by imagining different force loads on a single hand: An online feasibility study. J. Neuroeng. Rehabil. 2017, 14, 1–10. [Google Scholar] [CrossRef] [PubMed]
- Tung, S.W.; Guan, C.; Ang, K.K.; Phua, K.S.; Wang, C.; Kuah, C.W.K.; Chua, K.S.G.; Ng, Y.S.; Zhao, L.; Chew, E. A measurement of motor recovery for motor imagery-based BCI using EEG coherence analysis. In Proceedings of the 2015 10th International Conference on Information, Communications and Signal Processing (ICICS), Singapore, 2–4 December 2015; pp. 1–5. [Google Scholar]
- Lin, X.; Malik, W.Q.; Zhang, S. A novel hybrid BCI web browser based on SSVEP and eye-Tracking. In Proceedings of the 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), Nara, Japan, 17–19 October 2019; pp. 1–4. [Google Scholar]
- Xiao, J.; Xie, Q.; He, Y.; Yu, T.; Lu, S.; Huang, N.; Yu, R.; Li, Y. An auditory BCI system for assisting CRS-R behavioral assessment in patients with disorders of consciousness. Sci. Rep. 2016, 6, 1–13. [Google Scholar] [CrossRef] [Green Version]
- Friesen, C.L.; Bardouille, T.; Neyedli, H.F.; Boe, S.G. Combined action observation and motor imagery neurofeedback for modulation of brain activity. Front. Hum. Neurosci. 2017, 10, 692. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Wang, B.; Zhang, C.; Xiao, Y.; Wang, M.Y. An EEG/EMG/EOG-based multimodal human-machine interface to real-time control of a soft robot hand. Front. Neurorobotics 2019, 13, 7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huang, Q.; Zhang, Z.; Yu, T.; He, S.; Li, Y. An EEG-/EOG-based hybrid brain-computer interface: Application on controlling an integrated wheelchair robotic arm system. Front. Neurosci. 2019, 13, 1243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xiao, J.; Xie, Q.; Lin, Q.; Yu, T.; Yu, R.; Li, Y. Assessment of visual pursuit in patients with disorders of consciousness based on a brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2018, 26, 1141–1151. [Google Scholar] [CrossRef]
- Liu, Y.T.; Lin, Y.Y.; Wu, S.L.; Chuang, C.H.; Lin, C.T. Brain dynamics in predicting driving fatigue using a recurrent self-evolving fuzzy neural network. IEEE Trans. Neural Netw. Learn. Syst. 2015, 27, 347–360. [Google Scholar] [CrossRef]
- Marquez-Chin, C.; Marquis, A.; Popovic, M.R. EEG-triggered functional electrical stimulation therapy for restoring upper limb function in chronic stroke with severe hemiplegia. Case Rep. Neurol. Med. 2016, 2016, 9146213. [Google Scholar] [CrossRef] [Green Version]
- Jochumsen, M.; Navid, M.S.; Rashid, U.; Haavik, H.; Niazi, I.K. EMG-versus EEG-triggered electrical stimulation for inducing corticospinal plasticity. IEEE Trans. Neural Syst. Rehabil. Eng. 2019, 27, 1901–1908. [Google Scholar] [CrossRef]
- Li, Z.; Yuan, Y.; Luo, L.; Su, W.; Zhao, K.; Xu, C.; Huang, J.; Pi, M. Hybrid brain/muscle signals powered wearable walking exoskeleton enhancing motor ability in climbing stairs activity. IEEE Trans. Med. Robot. Bionics 2019, 1, 218–227. [Google Scholar] [CrossRef]
- Butt, M.; Naghdy, G.; Naghdy, F.; Murray, G.; Du, H. Investigating electrode sites for intention detection during robot based hand movement using EEG-BCI system. In Proceedings of the 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan, 29–31 October 2018; pp. 177–180. [Google Scholar]
- Butt, M.; Naghdy, G.; Naghdy, F.; Murray, G.; Du, H. Investigating the detection of intention signal during different exercise protocols in robot-assisted hand movement of stroke patients and healthy subjects using EEG-BCI system. Adv. Sci. Technol. Eng. Syst. J. 2019, 4, 300–307. [Google Scholar] [CrossRef] [Green Version]
- Pan, J.; Xie, Q.; Qin, P.; Chen, Y.; He, Y.; Huang, H.; Wang, F.; Ni, X.; Cichocki, A.; Yu, R.; et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain 2020, 143, 1177–1189. [Google Scholar] [CrossRef]
- Deng, X.; Yu, Z.L.; Lin, C.; Gu, Z.; Li, Y. Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation. J. Neural Eng. 2020, 17, 045005. [Google Scholar] [CrossRef] [PubMed]
- Kuo, C.H.; Chen, H.H.; Chou, H.C.; Chen, P.N.; Kuo, Y.C. Wireless stimulus-on-device design for novel P300 hybrid brain-computer interface applications. Comput. Intell. Neurosci. 2018, 2018, 2301804. [Google Scholar] [CrossRef] [PubMed]
- Tang, J.; Zhou, Z.; Yu, Y. A hybrid computer interface for robot arm control. In Proceedings of the 2016 8th International Conference on Information Technology in Medicine and Education (ITME), Fuzhou, China, 23–25 December 2016; pp. 365–369. [Google Scholar]
- Luu, T.P.; He, Y.; Brown, S.; Nakagome, S.; Contreras-Vidal, J.L. Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain–computer interface to a virtual reality avatar. J. Neural Eng. 2016, 13, 036006. [Google Scholar] [CrossRef] [PubMed]
- Sullivan, J.L.; Bhagat, N.A.; Yozbatiran, N.; Paranjape, R.; Losey, C.G.; Grossman, R.G.; Contreras-Vidal, J.L.; Francisco, G.E.; O’Malley, M.K. Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial. In Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017; pp. 122–127. [Google Scholar]
- Song, M.; Kim, J. Motor imagery enhancement paradigm using moving rubber hand illusion system. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea, 11–15 July 2017; pp. 1146–1149. [Google Scholar]
- Mastakouri, A.A.; Weichwald, S.; Özdenizci, O.; Meyer, T.; Schölkopf, B.; Grosse-Wentrup, M. Personalized brain-computer interface models for motor rehabilitation. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017; pp. 3024–3029. [Google Scholar]
- Ortiz, M.; Ferrero, L.; Iáñez, E.; Azorín, J.M.; Contreras-Vidal, J.L. Sensory integration in human movement: A new brain-machine interface based on gamma band and attention level for controlling a lower-limb exoskeleton. Front. Bioeng. Biotechnol. 2020, 8. [Google Scholar] [CrossRef] [PubMed]
- Tang, J.; Liu, Y.; Hu, D.; Zhou, Z. Towards BCI-actuated smart wheelchair system. Biomed. Eng. Online 2018, 17, 1–22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lóopez-Larraz, E.; Birbaumer, N.; Ramos-Murguialday, A. A hybrid EEG-EMG BMI improves the detection of movement intention in cortical stroke patients with complete hand paralysis. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18–21 July 2018; pp. 2000–2003. [Google Scholar]
- Song, M.; Kim, J. A paradigm to enhance motor imagery using rubber hand illusion induced by visuo-tactile stimulus. IEEE Trans. Neural Syst. Rehabil. Eng. 2019, 27, 477–486. [Google Scholar] [CrossRef]
- da Silva-Sauer, L.; Valero-Aguayo, L.; Velasco-Álvarez, F.; Fernández-Rodríguez, Á.; Ron-Angevin, R. A Shaping Procedure to Modulate Two Cognitive Tasks to Improve a Sensorimotor Rhythm-Based Brain-Computer Interface System. Span. J. Psychol. 2018, 21. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Yang, B.; Guan, C.; Li, B. A VR combined with MI-BCI application for upper limb rehabilitation of stroke. In Proceedings of the 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), Nanjing, China, 6–8 May 2019; Volume 1, pp. 1–4. [Google Scholar]
- Roy, G.; Nirola, D.; Bhaumik, S. An approach towards development of brain controlled lower limb exoskeleton for mobility regeneration. In Proceedings of the 2019 IEEE Region 10 Symposium (TENSYMP), Kolkata, India, 7–9 June 2019; pp. 385–390. [Google Scholar]
- Antelis, J.M.; Montesano, L.; Ramos-Murguialday, A.; Birbaumer, N.; Minguez, J. Decoding upper limb movement attempt from EEG measurements of the contralesional motor cortex in chronic stroke patients. IEEE Trans. Biomed. Eng. 2016, 64, 99–111. [Google Scholar] [CrossRef]
- Bhagat, N.A.; Venkatakrishnan, A.; Abibullaev, B.; Artz, E.J.; Yozbatiran, N.; Blank, A.A.; French, J.; Karmonik, C.; Grossman, R.G.; O’Malley, M.K.; et al. Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors. Front. Neurosci. 2016, 10, 122. [Google Scholar] [CrossRef]
- Luu, T.P.; He, Y.; Nakagome, S.; Contreras-Vidal, J.L. EEG-based brain-computer interface to a virtual walking avatar engages cortical adaptation. In Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, 5–8 October 2017; pp. 3054–3057. [Google Scholar]
- Uma, M.; Prabhu, S. A feasibility study of BCI based FES model for differently abled people. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2018; Volume 402, p. 12009. [Google Scholar]
- Narayanan, S.; Divya, K. A Sensor based mechanism for controlling mobile robots with ZigBee. In Computational Intelligence in Data Mining—Volume 2; Springer: Berlin/Heidelberg, Germany, 2016; pp. 1–12. [Google Scholar]
- Contreras-Castañeda, M.A.; Holgado-Terriza, J.A.; Pomboza-Junez, G.; Paderewski-Rodríguez, P.; Gutiérrez-Vela, F.L. Smart Home: Multimodal Interaction for Control of Home Devices. In Proceedings of the International Conference on Human Computer Interaction 2019, Donostia Gipuzkoa, Spain, 25–28 June 2019; pp. 1–8. [Google Scholar]
- Shukla, A.K. The illusive man. In Proceedings of the 2016 International Conference on Systems in Medicine and Biology (ICSMB), Kharagpur, India, 4–7 January 2016; pp. 119–123. [Google Scholar]
- Pinheiro, O.R.; Alves, L.R.; Romero, M.; de Souza, J.R. Wheelchair simulator game for training people with severe disabilities. In Proceedings of the 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW), Vila Real, Portugal, 1–3 December 2016; pp. 1–8. [Google Scholar]
- Raj, R.; Deb, S.; Bhattacharya, P. Brain computer interfaced single key omni directional pointing and command system: A screen pointing interface for differently-abled person. Procedia Comput. Sci. 2018, 133, 161–168. [Google Scholar] [CrossRef]
- Stephygraph, L.R.; Arunkumar, N. Brain-actuated wireless mobile robot control through an adaptive human–machine interface. In Proceedings of the International Conference on Soft Computing Systems; Springer: Berlin/Heidelberg, Germany, 2016; pp. 537–549. [Google Scholar]
- Permana, K.; Wijaya, S.; Prajitno, P. Controlled wheelchair based on brain computer interface using Neurosky Mindwave Mobile 2. AIP Confe. Proc. 2019, 2168, 020022. [Google Scholar]
- Xin, L.; Gao, S.; Tang, J.; Xu, X. Design of a Brain Controlled Wheelchair. In Proceedings of the 2018 IEEE 4th International Conference on Control Science and Systems Engineering (ICCSSE), Wuhan, China, 21–23 August 2018; pp. 112–116. [Google Scholar]
- Dev, A.; Rahman, M.A.; Mamun, N. Design of an EEG-based brain controlled wheelchair for quadriplegic patients. In Proceedings of the 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India, 6–8 April 2018; pp. 1–5. [Google Scholar]
- Blandón, D.Z.; Muñoz, J.E.; Lopez, D.S.; Gallo, O.H. Influence of a BCI neurofeedback videogame in children with ADHD. Quantifying the brain activity through an EEG signal processing dedicated toolbox. In Proceedings of the 2016 IEEE 11th Colombian Computing Conference (CCC), Popayan, Colombia, 27–30 September 2016; pp. 1–8. [Google Scholar]
- Waheed, S.A.; Khader, P.S.A. IoT based approach for detection of dominating emotions in persons who stutter. In Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 7–9 October 2020; pp. 14–18. [Google Scholar]
- Punsawad, Y.; Ngamrussameewong, S.; Wongsawat, Y. On the development of BCI and its neurofeedback training system for assistive communication device in persons with severe disability. In Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea, 13–16 December 2016; pp. 1–4. [Google Scholar]
- Bertomeu-Motos, A.; Ezquerro, S.; Barios, J.A.; Lledó, L.D.; Domingo, S.; Nann, M.; Martin, S.; Soekadar, S.R.; Garcia-Aracil, N. User activity recognition system to improve the performance of environmental control interfaces: A pilot study with patients. J. Neuroeng. Rehabil. 2019, 16, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gaxiola-Tirado, J.A.; Iáñez, E.; Ortíz, M.; Gutiérrez, D.; Azorín, J.M. Effects of an exoskeleton-assisted gait motor imagery training in functional brain connectivity. In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 429–432. [Google Scholar]
- Vourvopoulos, A.; Pardo, O.M.; Lefebvre, S.; Neureither, M.; Saldana, D.; Jahng, E.; Liew, S.L. Effects of a brain-computer interface with virtual reality (VR) neurofeedback: A pilot study in chronic stroke patients. Front. Hum. Neurosci. 2019, 13, 210. [Google Scholar] [CrossRef] [Green Version]
- Vourvopoulos, A.; Jorge, C.; Abreu, R.; Figueiredo, P.; Fernandes, J.C.; Bermúdez i Badia, S. Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: A clinical case report. Front. Hum. Neurosci. 2019, 13, 244. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Badesa, F.J.; Diez, J.A.; Barios, J.A.; Catalan, J.M.; Garcia-Aracil, N. Evaluation of performance and heart rate variability during intensive usage of a BCI-controlled hand exoskeleton. In Proceedings of the 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA, 29 November–1 December 2020; pp. 164–169. [Google Scholar]
- Rodríguez-Ugarte, M.; Iáñez, E.; Ortiz, M.; Azorín, J.M. Improving real-time lower limb motor imagery detection using tDCS and an exoskeleton. Front. Neurosci. 2018, 12, 757. [Google Scholar] [CrossRef] [Green Version]
- Quiles, E.; Suay, F.; Candela, G.; Chio, N.; Jiménez, M.; Álvarez-Kurogi, L. Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation. Int. J. Environ. Res. Public Health 2020, 17, 699. [Google Scholar] [CrossRef] [Green Version]
- Rodríguez-Ugarte, M.; Iáñez, E.; Ortíz, M.; Azorín, J.M. Personalized offline and pseudo-online BCI models to detect pedaling intent. Front. Neuroinformatics 2017, 11, 45. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Peterson, V.; Galván, C.; Hernández, H.; Spies, R. A feasibility study of a complete low-cost consumer-grade brain-computer interface system. Heliyon 2020, 6, e03425. [Google Scholar] [CrossRef] [PubMed]
- Romero-Laiseca, M.A.; Delisle-Rodriguez, D.; Cardoso, V.; Gurve, D.; Loterio, F.; Nascimento, J.H.P.; Krishnan, S.; Frizera-Neto, A.; Bastos-Filho, T. A low-cost lower-limb brain-machine interface triggered by pedaling motor imagery for post-stroke patients rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2020, 28, 988–996. [Google Scholar] [CrossRef]
- Anil, D.G.; Pelayo, P.; Mistry, K.S.; George, K. A tactile P300 based brain computer interface system for communication in iOS devices. In Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA, 14–17 May 2018; pp. 1–6. [Google Scholar]
- Casey, A.; Azhar, H.; Grzes, M.; Sakel, M. BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients. Disabil. Rehabil. Assist. Technol. 2019, 1–13. [Google Scholar] [CrossRef]
- Wang, K.J.; Zhang, L.; Luan, B.; Tung, H.W.; Liu, Q.; Wei, J.; Sun, M.; Mao, Z.H. Brain-computer interface combining eye saccade two-electrode EEG signals and voice cues to improve the maneuverability of wheelchair. In Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017; pp. 1073–1078. [Google Scholar]
- Samson, V.; Kitti, B.P.; Kumar, S.P.; Babu, D.S.; Monica, C. Electroencephalogram-based OpenBCI devices for disabled people. In Proceedings of the 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications; Springer: Berlin/Heidelberg, Germany, 2018; pp. 229–238. [Google Scholar]
- Chiu, M.; Murthy, H.; George, K. Mobile switch control using auditory and haptic steady state response in Ear-EEG. In Proceedings of the 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA, 8–10 November 2018; pp. 1032–1037. [Google Scholar]
- Perera, C.J.; Naotunna, I.; Sadaruwan, C.; Gopura, R.A.R.C.; Lalitharatne, T.D. SSVEP based BMI for a meal assistance robot. In Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 9–12 October 2016; pp. 002295–002300. [Google Scholar]
- Long, X.; Ma, Y.; Ma, X.; Yan, Z.; Wang, C.; Wu, X. The EEG-based lower limb exoskeleton system optimization strategy based on channel selection. In Proceedings of the 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR), Irkutsk, Russia, 4–9 August 2019; pp. 1–6. [Google Scholar]
- Long, X.; Liu, D.X.; Liang, S.; Yan, Z.; Wu, X. An eeg-based bci system for controlling lower exoskeleton to step over obstacles in realistic walking situation. In Proceedings of the 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, 18–21 November 2018; pp. 1609–1614. [Google Scholar]
- Duan, S.; Wang, C.; Li, M.; Long, X.; Wu, X.; Feng, W. Haptic and visual enhance-based motor imagery BCI for rehabilitation lower-limb exoskeleton. In Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, 6–8 December 2019; pp. 2025–2030. [Google Scholar]
- Wang, Z.; Wang, C.; Lv, Q.; Wu, G.; Zhang, T.; Wu, X. Implementation of brain-computer interface based on SSVEP for control of a lower-limb exoskeleton. In Proceedings of the 2016 IEEE International Conference on Information and Automation (ICIA), Ningbo, China, 1–3 August 2016; pp. 1882–1886. [Google Scholar]
- Rimbert, S.; Bougrain, L.; Fleck, S. Learning how to generate kinesthetic motor imagery using a BCI-based learning environment: A comparative study based on guided or trial-and-error approaches. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 2483–2498. [Google Scholar]
- Yazmir, B.; Reiner, M. Monitoring brain potentials to guide neurorehabilitation of tracking impairments. In Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), London, UK, 17–20 July 2017; pp. 983–988. [Google Scholar]
- Liu, X.; Liang, S.; Hang, W.; Lei, B.; Wang, Q.; Qin, J.; Choi, K.S. Performance evaluation of walking imagery training based on virtual environment in brain-computer interfaces. In Proceedings of the 2017 IEEE International Symposium on Multimedia (ISM), Taichung, Taiwan, 11–13 December 2017; pp. 25–30. [Google Scholar]
- Ganin, I.; Kosichenko, E.; Sokolov, A.; Ioannisyanc, O.; Arefev, I.; Basova, A.Y.; Kaplan, A.Y. Adapting the p300 brain-computer interface technology to assess condition of anorexia nervosa patients. Bull. Russ. State Med. Univ. 2019. [Google Scholar] [CrossRef] [Green Version]
- Frolov, A.A.; Mokienko, O.; Lyukmanov, R.; Biryukova, E.; Kotov, S.; Turbina, L.; Nadareyshvily, G.; Bushkova, Y. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial. Front. Neurosci. 2017, 11, 400. [Google Scholar] [CrossRef] [Green Version]
- Gordleeva, S.Y.; Lobov, S.A.; Grigorev, N.A.; Savosenkov, A.O.; Shamshin, M.O.; Lukoyanov, M.V.; Khoruzhko, M.A.; Kazantsev, V.B. Real-time EEG–EMG human–machine interface-based control system for a lower-limb exoskeleton. IEEE Access 2020, 8, 84070–84081. [Google Scholar] [CrossRef]
- Kotov, S.; Turbina, L.; Bobrov, P.; Frolov, A.; Pavlova, O.; Kurganskaya, M.; Biryukova, E. Rehabilitation of stroke patients with a bioengineered “brain–computer interface with exoskeleton” system. Neurosci. Behav. Physiol. 2016, 46, 518–522. [Google Scholar] [CrossRef]
- Kiselev, A.R.; Maksimenko, V.A.; Shukovskiy, N.; Pisarchik, A.N.; Pitsik, E.; Hramov, A.E. Post-stroke rehabilitation with the help of brain-computer interface. In Proceedings of the 2019 3rd School on Dynamics of Complex Networks and Their Application in Intellectual Robotics (DCNAIR), Innopolis, Russia, 9–11 September 2019; pp. 83–85. [Google Scholar]
- Grubov, V.; Kiselev, A.; Badarin, A.; Schukovsky, N. Braincomputer interface for post-stroke rehabilitation. Cybern. Phys. 2019, 8, 251–256. [Google Scholar] [CrossRef] [Green Version]
- Zhuralvev, M.; Runnova, A.; Kiselev, A. Characteristics of post-stroke patients brain activity with real and imagined movements in the BCI-rehabilitation process. Procedia Comput. Sci. 2020, 169, 677–685. [Google Scholar] [CrossRef]
- Latif, M.Y.; Naeem, L.; Hafeez, T.; Raheel, A.; Saeed, S.M.U.; Awais, M.; Alnowami, M.; Anwar, S.M. Brain computer interface based robotic arm control. In Proceedings of the 2017 International Smart Cities Conference (ISC2), Wuxi, China, 14–17 September 2017; pp. 1–5. [Google Scholar]
- Ndulue, C.; Orji, R. Driving Persuasive Games with Personal EEG Devices: Strengths and Weaknesses. In Proceedings of the Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, Larnaca, Cyprus, 9–12 June 2019; pp. 173–177. [Google Scholar]
- Meza, A.; Baltazar, R.; Casillas, M.; Zamudio, V.; Mosiño, F.; Serna, B. System Development for Automatic Control Using BCI. In Agents and Multi-agent Systems: Technologies and Applications 2019; Springer: Berlin/Heidelberg, Germany, 2020; pp. 175–184. [Google Scholar]
- Chowdhury, M.A.S.; Saha, D.K. Processing of motor imagery EEG Signals for controlling the opening and the closing of artificial hand. In Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 20–22 December 2019; pp. 1–5. [Google Scholar]
- Quiroz, G.; Valdez, A.E.; Ruiz, R.S.; Mercado, L. Coherence analysis of EEG in locomotion using graphs. Mex. J. Biomed. Eng. 2017, 38, 235–246. [Google Scholar]
- Murakami, M.; Nakatani, S.; Araki, N.; Konishi, Y.; Mabuchi, K. Motion discrimination from EEG using logistic regression and Schmitt-trigger-type threshold. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, 9–12 October 2015; pp. 2338–2342. [Google Scholar]
- Mishchenko, Y.; Kaya, M.; Ozbay, E.; Yanar, H. Developing a three-to six-state EEG-based brain–computer interface for a virtual robotic manipulator control. IEEE Trans. Biomed. Eng. 2018, 66, 977–987. [Google Scholar] [CrossRef] [PubMed]
- Kuhner, D.; Fiederer, L.D.J.; Aldinger, J.; Burget, F.; Völker, M.; Schirrmeister, R.T.; Do, C.; Boedecker, J.; Nebel, B.; Ball, T.; et al. A service assistant combining autonomous robotics, flexible goal formulation, and deep-learning-based brain–computer interfacing. Robot. Auton. Syst. 2019, 116, 98–113. [Google Scholar] [CrossRef]
- Bastos-Filho, T.; Romero, M.; Cardoso, V.; Pomer, A.; Longo, B.; Delisle, D. A setup for lower-limb post-stroke rehabilitation based on motor imagery and motorized pedal. In Latin American Conference on Biomedical Engineering; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1125–1129. [Google Scholar]
- McCrimmon, C.M.; Wang, M.; Lopes, L.S.; Wang, P.T.; Karimi-Bidhendi, A.; Liu, C.Y.; Heydari, P.; Nenadic, Z.; Do, A.H. A small, portable, battery-powered brain-computer interface system for motor rehabilitation. In Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016; pp. 2776–2779. [Google Scholar]
- Lee, S.H.; Kim, S.S.; Lee, B.H. Action observation training and brain-computer interface controlled functional electrical stimulation enhance upper extremity performance and cortical activation in patients with stroke: A randomized controlled trial. Physiother. Theory Pract. 2020, 1–9. [Google Scholar] [CrossRef]
- Melinscak, F.; Montesano, L.; Minguez, J. Asynchronous detection of kinesthetic attention during mobilization of lower limbs using EEG measurements. J. Neural Eng. 2016, 13, 016018. [Google Scholar] [CrossRef]
- Mokienko, O.; Lyukmanov, R.K.; Chernikova, L.; Suponeva, N.; Piradov, M.; Frolov, A. Brain–computer interface: The first experience of clinical use in Russia. Hum. Physiol. 2016, 42, 24–31. [Google Scholar] [CrossRef]
- Lisi, G.; Hamaya, M.; Noda, T.; Morimoto, J. Dry-wireless EEG and asynchronous adaptive feature extraction towards a plug-and-play co-adaptive brain robot interface. In Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16–21 May 2016; pp. 959–966. [Google Scholar]
- Qin, Z.; Xu, Y.; Shu, X.; Hua, L.; Sheng, X.; Zhu, X. eConhand: A wearable brain-computer interface system for stroke rehabilitation. In Proceedings of the 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA, 20–23 March 2019; pp. 734–737. [Google Scholar]
- Mrachacz-Kersting, N.; Jiang, N.; Stevenson, A.J.T.; Niazi, I.K.; Kostic, V.; Pavlovic, A.; Radovanovic, S.; Djuric-Jovicic, M.; Agosta, F.; Dremstrup, K.; et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J. Neurophysiol. 2016, 115, 1410–1421. [Google Scholar] [CrossRef] [PubMed]
- Jia, T.; Li, C.; Guan, X.; Ji, L. Enhancing engagement during robot-assisted rehabilitation integrated with motor imagery task. In Proceedings of the 2019 International Conference on Intelligent Medicine and Health, Ningbo, China, 1–3 July 2019; pp. 12–16. [Google Scholar]
- Lim, H.; Ku, J. Flickering exercise video produces mirror neuron system (MNS) activation and steady state visually evoked potentials (SSVEPs). Biomed. Eng. Lett. 2017, 7, 281–286. [Google Scholar] [CrossRef] [PubMed]
- Chai, R.; Naik, G.R.; Ling, S.H.; Nguyen, H.T. Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems. Biomed. Eng. Online 2017, 16, 1–23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kaur, M.; Singh, B. Implementation of SSVEP technology to develop assistive devices. In Progress in Advanced Computing and Intelligent Engineering; Springer: Berlin/Heidelberg, Germany, 2018; pp. 687–699. [Google Scholar]
- Lu, R.R.; Zheng, M.X.; Li, J.; Gao, T.H.; Hua, X.Y.; Liu, G.; Huang, S.H.; Xu, J.G.; Wu, Y. Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients. Neurosci. Lett. 2020, 718, 134727. [Google Scholar] [CrossRef]
- Norman, S.L.; Dennison, M.; Wolbrecht, E.; Cramer, S.C.; Srinivasan, R.; Reinkensmeyer, D.J. Movement anticipation and EEG: Implications for BCI-contingent robot therapy. IEEE Trans. Neural Syst. Rehabil. Eng. 2016, 24, 911–919. [Google Scholar] [CrossRef] [Green Version]
- Ferreira, A.L.S.; de Miranda, L.C.; de Miranda, E.E.C.; Sakamoto, S.G. A survey of interactive systems based on brain-computer interfaces. SBC J. Interact. Syst. 2013, 4, 3–13. [Google Scholar] [CrossRef]
- Soufineyestani, M.; Dowling, D.; Khan, A. Electroencephalography (EEG) Technology Applications and Available Devices. Appl. Sci. 2020, 10, 7453. [Google Scholar] [CrossRef]
- Li, M.; Liu, Y.; Wu, Y.; Liu, S.; Jia, J.; Zhang, L. Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training. Int. J. Neurosci. 2014, 124, 403–415. [Google Scholar] [CrossRef] [PubMed]
- Villa-Parra, A.; Delisle-Rodríguez, D.; López-Delis, A.; Bastos-Filho, T.; Sagaró, R.; Frizera-Neto, A. Towards a robotic knee exoskeleton control based on human motion intention through EEG and sEMGsignals. Procedia Manuf. 2015, 3, 1379–1386. [Google Scholar] [CrossRef] [Green Version]
- Belkacem, A.N. Cybersecurity framework for P300-based brain computer interface. In Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 11–14 October 2020; pp. 1–6. [Google Scholar]
- Lepa, S.; Steffens, J.; Herzog, M.; Egermann, H. Popular Music as Entertainment Communication: How Perceived Semantic Expression Explains Liking Previously Unknown Music. Media Commun. 2020. [Google Scholar] [CrossRef]
- van de Laar, B.; Gürkök, H.; Bos, D.P.O.; Poel, M.; Nijholt, A. Experiencing BCI control in a popular computer game. IEEE Trans. Comput. Intell. AI Games 2013, 5, 176–184. [Google Scholar] [CrossRef] [Green Version]
- Ratti, E.; Waninger, S.; Berka, C.; Ruffini, G.; Verma, A. Comparison of medical and consumer wireless EEG systems for use in clinical trials. Front. Hum. Neurosci. 2017, 11, 398. [Google Scholar] [CrossRef] [Green Version]
- Ramadan, R.A.; Vasilakos, A.V. Brain computer interface: Control signals review. Neurocomputing 2017, 223, 26–44. [Google Scholar] [CrossRef]
- Kasim, M.A.A.; Low, C.Y.; Ayub, M.A.; Zakaria, N.A.C.; Salleh, M.H.M.; Johar, K.; Hamli, H. User-friendly labview gui for prosthetic hand control using emotiv eeg headset. Procedia Comput. Sci. 2017, 105, 276–281. [Google Scholar] [CrossRef]
- Li, K.; Sankar, R.; Arbel, Y.; Donchin, E. P300 based single trial independent component analysis on EEG signal. In International Conference on Foundations of Augmented Cognition; Springer: Berlin/Heidelberg, Germany, 2009; pp. 404–410. [Google Scholar]
- Mihajlović, V.; Grundlehner, B.; Vullers, R.; Penders, J. Wearable, wireless EEG solutions in daily life applications: What are we missing? IEEE J. Biomed. Health Inform. 2014, 19, 6–21. [Google Scholar] [CrossRef] [PubMed]
Research Question | Rationale | |
---|---|---|
RQ1 | What are the publication trends based on EEG equipment? | To identify the sector that collaborates with BCI technology using the EEG method |
RQ2 | What are the most common types (wired or wireless) of noninvasive EEG-based BCI equipment that have been used in brain studies? | To discover whether the EEG equipment mostly uses the wired or wireless type based on a specific research area |
Company | Type of EEG Equipment | Research Area | |
---|---|---|---|
Wired | Wireless | ||
Advanced Brain Monitoring | × | B-Alert® X10 | EG [66], MD [253,254] |
B-Alert X24 | ET [79] | ||
ANT Neuro | × | eegosports | ET [77] |
eego™rt | MD [266] | ||
BIOPAC Systems Inc. | EEG100C | × | MD [259] |
Biosemi | ActiveTwo | × | ET [75], MD [236,237,238,239,240,241,242] |
Brain Products | actiCAP system | MD [194,199,205] | |
actiHamp | MD [192,198,201,203] | ||
BrainAmp | EG [57], ET [78], MD [195,196,197,200,204] | ||
BrainVision | EG [58] | ||
capTrak | MD [206] | ||
QuickAmp USB | EG [56] | ||
V-amp | MD [191] | ||
MOVE system | MD [193] | ||
Cognionics Inc. | × | HD-72 EEG | EG [63] |
Quick-20 | MD [263] | ||
Compumedics Neuroscan | Grael | × | ET [67,68], MD [187,188] |
NuAmps | MD [167,168,169,170,172,176,180,183,185,186,189,190] | ||
SynAmps | EG [59,60], ET [69], MD [171,173,174,175,177,178,179,181,182,184] | ||
Electrical Geodesics Inc. | Geodesic EEG System 400 | × | EG [64], MD [271] |
Emotiv | × | Emotiv EPOC | ED [37,39,40], EG [5,44,45,46,47,48,49,50], ET [70,71,72], MD [134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,162,164,165,166] |
Emotiv Insight | ED [36,38], MD [161,163] | ||
g.Tec | g.BSamp | MD [110] | |
g.Hiamp | MD [85,97,100,106,127,132] | ||
g.USBamp | EG [52,54], ET [76], MD [80,81,82,83,84,87,89,92,94,95,96,98,99,101,102,104,105,107,108,111,112,114,117,118,119,120,121,122,125,126,128,130,131,133] | ||
g.MOBIlab+® | MD [86,93,103,113,115,129] | ||
g.Nautilus | EG [51], MD [88,90,91,109,116,123,124] | ||
Unicorn Hybrid Black | EG [55] | ||
InteraXon | × | Muse headband | ED [41,42,43], MD [250,251,252] |
Jingahi | × | JAGA16 | MD [264] |
Jordan NeuroScience Inc. | BrainNet | × | MD [258] |
Laxtha Inc. | PolyG-I | × | MD [260] |
Medical Computer Systems | NVX52 | × | MD [243,244,245,246] |
Medicom MTD | Encephalan-EEGR-19/26 | × | MD [247,248,249] |
Mega Electronic | NeurOne | × | MD [257] |
MindMedia | Nexus10 Biosignal | × | MD [269] |
NCC Medical Co., | NGERP-P | × | MD [270] |
NeuroBioLab | NBL640 | × | MD [262] |
Netech | × | MinSim300 | MD [268] |
Neuroelectrics | × | Enobio 8 | EG [65], MD [220,223,224,226] |
Enobio 32 | MD [227] | ||
StarSim 8 | MD [222] | ||
StarSim R32 | MD [221,225] | ||
NeuroSky | × | BrainWave | MD [208,210] |
MindFlex | MD [219] | ||
MindWave Mobile | EG [61,62], MD [207,209,211,212,213,214,216,217,218] | ||
ThinkGear AM (TGAM) | MD [215] | ||
Nihon Kohden | JE-921A | × | MD [256] |
AB-611J | MD [255] | ||
OpenBCI | × | OpenBCI 32 bit | MD [232,235] |
Open BCI Cyton | ET [73,74], MD [228,229,231,234] | ||
OpenBCI Ganglion | MD [233] | ||
Ultracortex BCI | MD [230] | ||
TMSi | Refa 32 | × | MD [261] |
VIASYS Healthcare | Nicolet 1 | × | MD [265] |
Wearable Sensing | × | DSI-24 | MD [267] |
Company | EEG Equipment | Medical Certificate | Recommendation | Electrode Number, Type and Placement | Additional Sensor (Optional) | Size and Shape (Cap) | Approximated 2021 Cost | No. of Publication |
---|---|---|---|---|---|---|---|---|
Advanced Brain Monitoring | B-Alert® X10 | ISO 13485, CE, FDA | Neuromarketing, BCI, identify biomarkers | 9 channels, electrolyte cream, and cover whole brain | ECG, EMG, EOG | Adjustable (from adolescent to adult) | $9950 up to $14,950 | 4 |
B-Alert X24 | 20 channels, electrolyte cream, and cover whole brain | |||||||
ANT Neuro | eegosports eego™rt | CE, FDA | BCI, neurofeedback, neurorehabilitation, neurogaming | 8 to 64 channels, gel/soft dry and cover whole brain | EMG, physiological sensor | 6 sizes | - | 2 |
BIOPAC Systems Inc. | EEG100C | X | Epilepsy, tumor pathology, sleep studies, evoked responses, cognition studies. | 16 channels, wet and cover whole brain | X | 4 sizes | $2000 | 1 |
Biosemi | ActiveTwo | X | Electrophysiology research | 16 to 256 channels, gel and cover whole brain | EMG, ECG | 15 sizes | 17,000 up to 75,000 | 8 |
Brain Products | actiCAP system actiHamp BrainAmp BrainVision capTrak MOVE system | X | Neuroscience, neurofeedback, neurophysiological | 8 to 256 channels, gel/dry and cover whole brain | EOG, EMG | 14 sizes | $12,000 to $28,500 | 20 |
QuickAmp USB V-amp | No longer available | |||||||
Cognionics Inc. | HD-72 EEG | X | Neurofeedback, neurodiagnostic | 64 channels, dry and cover whole brain | ECG, EMG, EOG, RESP, GSR | Adjustable | $14,500 up to $26,000 | 2 |
Quick-20 | 21 channels, dry electrode and whole brain | |||||||
Compumedics Neuroscan | Grael NuAmps SynAmps | FDA | Clinical neuro-diagnostics, research | up to 256 channels, gel/saline and cover whole brain | EOG, ECG, EMG | 5 sizes | - | 29 |
Electrical Geodesics Inc. | Geodesic EEG System 400 | FDA | Clinical applications | up to 256 channels, saline and cover whole brain | ECG | Available in sizes from infant to adult | - | 2 |
Emotiv | Emotiv EPOC | X | Research, personal use | 14 channels, saline soaked felt pads and cover whole brain | Quaternions, accelerometer, magnetometer | Adjustable | $299 up to $849 | 49 |
Emotiv Insight | 5 channels, semi dry and cover frontal, temporal and parietal | |||||||
g.Tec | g.BSamp g.Hiamp g.USBamp g.MOBIlab+® g.Nautilus Unicorn Hybrid Black | ISO 14971, FDA | BCI, neuroscience, neurotechnology | up to 256 channels, dry/gel and cover whole brain | ECoG, ECG, EMG, EOG, accelerometer, external body sensor | 3 sizes | 1000(Unicorn Hybrid Black) to 30,000(customize) | 60 |
Jingahi | JAGA16 | X | Neuroscience, suitable for rat | 16 channels and cover whole brain | X | Standard size | - | 1 |
Jordan NeuroScience Inc. | BrainNet | FDA | Neurodiagnostic, neurofeedback | 14 to 21 channels, cream and cover whole brain | X | 4 sizes | - | 1 |
Laxtha Inc. | PolyG-I | ISO 13485, KFDA | Scientific research, forensic science | 8 channels and prefrontal area | ECG, EMG, PPG, GSR, RESP | Standard size | - | 1 |
Medical Computer Systems | NVX52 | ISO 13485 | Research for any application | 48 channels, wet and cover whole brain | Any biosensor | 9 sizes | 4860 | 5 |
Medicom MTD | Encephalan-EEGR-19/26 | ISO 13485 | Neurology, neurophysiology, epileptology, sleep studies, scientific research | 20 channels, wet and cover whole brain | EOG, ECG, EMG | 5 sizes | $5000 up to $40,000 | 3 |
Mega Electronic | NeurOne | European Medical Directive 93/42/EEC, ISO 13485, CE | Neuroscience, psychological aplication | 32 to 128 channels and cover whole brain | Gyro, EMG, GSR, accelerometer, | Standard size | - | 1 |
MindMedia | Nexus10 Biosignal | CE, FDA | Biofeedback, neurofeedback, psychophysiological research. | 4 channels, electrogel and cover whole brain | EMG, EOG, ECG | 4 sizes | 1050 | 1 |
Interaxon | Muse headband | X | EEG-powered sleep, tracking, meditation | 2 channels, dry and cover frontal lobe | Gyro, PPG, accelerometer | Adjustable | $294.98 up to $369.98 | 6 |
NCC Medical Co. | NGERP-P | ISO 13485 | - | 24 channels and cover whole brain | - | Standard size | - | 1 |
NeuroBioLab | NBL640 | - | Neurobiofeedback | 24 channels, dry/gel electrode and cover whole brain | X | Standard size | - | 1 |
Netech | MinSim300 | CE, FDA | Recorders, sleep study monitors | 10 channels | - | Standard size | - | 1 |
Neuroelectrics | Enobio 8 Enobio 32 StarSim 8 StarSim R32 | CE, FDA | Neuroscience, BCI, neurogaming, neurofeedback | 8 to 32 channels, dry/gel and cover whole brain | Accelerometer | 6 sizes | - | 9 |
NeuroSky | BrainWave MIndFlex MindWave ThinkGearAM (TGAM) | X | BCI, neurogaming, neurofeedback, neuroscience, meditation | 1 channel, dry and cover frontal lobe | ECG | Adjustable | $109.99 | 15 |
Nihon Kohden | JE-921A AB-611J | ISO 13485, MDSAP | Epilepsy monitoring, medical research | 5 to 32 channels and cover whole brain | Oximetry | 4 sizes and 1 adjustable silicone cap | - | 2 |
OpenBCI | OpenBCI 32bit Open BCI Cyton OpenBCI Ganglion Ultracortex BCI | X | BCI, biosensing, neurofeedback | 4 to 21 channels, dry/gel and cover whole brain | EMG, ECG, accelerometer | 3 sizes and 3D printable can be adjust | up to $3200 | 10 |
TMSi | Refa 32 | No longer available | 1 | |||||
VIASYS Healthcare | Nicolet 1 | FDA | Respiratory care, neuroscience, medical, surgical care | 32 to 44 channels and cover whole brain | EMG | Standard size | - | 1 |
Wearable Sensing | DSI-24 | X | Psychological research, neuroscience, neuromarketing, BCI, neurogaming, neurofeedback | 21 channels, dry and cover whole brain | ECG, EMG, EOG | Adjustable | 20,000 | 1 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jamil, N.; Belkacem, A.N.; Ouhbi, S.; Lakas, A. Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review. Sensors 2021, 21, 4754. https://doi.org/10.3390/s21144754
Jamil N, Belkacem AN, Ouhbi S, Lakas A. Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review. Sensors. 2021; 21(14):4754. https://doi.org/10.3390/s21144754
Chicago/Turabian StyleJamil, Nuraini, Abdelkader Nasreddine Belkacem, Sofia Ouhbi, and Abderrahmane Lakas. 2021. "Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review" Sensors 21, no. 14: 4754. https://doi.org/10.3390/s21144754
APA StyleJamil, N., Belkacem, A. N., Ouhbi, S., & Lakas, A. (2021). Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review. Sensors, 21(14), 4754. https://doi.org/10.3390/s21144754