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Special Issue "Brain–Computer Interfaces: Advances and Challenges"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 19208

Special Issue Editors

Dr. Miguel Ángel López Gordo
E-Mail Website
Guest Editor
Brain-Computer Interfaces Lab (CITIC-UGR), Smart Wireless Applications and Technologies Group (SWAT), Department of Signal Theory, Telematics and Communications, University of Granada, 18014 Granada, Spain
Interests: neural interfaces; neurometry; biosignal processing; neuro-devices; brain area networks
Special Issues, Collections and Topics in MDPI journals
Dr. Christian A. Morillas Gutiérrez
E-Mail Website
Guest Editor
Brain-computer Interface Lab at Research Centre for Information and Communications Technologies (CITIC-UGR) Circuits and Systems for Information Processing Group (CASIP), Department of Computer Architecture and Computer Technology, University of Granada,18014 Granada, Spain
Interests: bio-signal acquisition devices; neurometry; bio-signal processing; neuro-devices

Special Issue Information

Dear Colleagues,

Brain–computer Interfaces emerged a few decades ago as a new communication technology to permit subjects with severe neuromuscular disorders to communicate and interact with the outer world. Recently, the rapid development of wireless technology opened the door for out-of-the-lab applications, such as in the field of entertainment, industry, marketing, and education. In the future, we foresee a new generation of smart neuro-devices for the cloud-computing of cerebral activity. This includes the deployment of large ensembles of fully-wireless-neural prostheses capable of performing complex tasks and bidirectional sensing. This ecosystem of implantable neuro-sensors constitutes a new framework of the IoT arena, referred to as brain area networks (BRAN). BRAN is a challenging and multidisciplinary paradigm that encompasses communication protocols, efficient wireless energy harvesting, and large-scale neuro-signal acquisition and processing.

This Special Issue will explore the advances, challenges, and future prospects of both invasive and non-invasive brain–computer interfaces (e.g., innovative multi bio-signals headsets, integrated stimulation-acquisition devices, dry electrodes, implantable neuro-technologies, and sensors), services and applications (e.g., applications in telemedicine and telerehabilitation, entertainment, cognitive workload assessment and emotion recognition, neuro-rehabilitation, ambient assisted living, biosecurity, clinical evaluation, neuro-marketing, and education, among others), scalable bio-signals processing algorithms for large numbers of neuro-sensors, including cloud-computing and closed-loop systems.

Dr. Miguel Ángel López Gordo
Dr. Christian A. Morillas Gutiérrez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Neuro-signal processing, implantable
  • Brain-computer interfaces and applications
  • Neurometry and applications (education, marketing, industry, military, emotions recognition, telemedicine, rehabilitation, e-sports, and brain activity monitoring, among others)
  • Methods for neuro-signal processing and analysis
  • Communication techniques and power harvesting for brain area networks

Published Papers (12 papers)

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Research

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Article
A High-Density EEG Study Investigating VR Film Editing and Cognitive Event Segmentation Theory
Sensors 2021, 21(21), 7176; https://doi.org/10.3390/s21217176 - 28 Oct 2021
Viewed by 840
Abstract
This paper introduces a cognitive psychological experiment that was conducted to analyze how traditional film editing methods and the application of cognitive event segmentation theory perform in virtual reality (VR). Thirty volunteers were recruited and asked to watch a series of short VR [...] Read more.
This paper introduces a cognitive psychological experiment that was conducted to analyze how traditional film editing methods and the application of cognitive event segmentation theory perform in virtual reality (VR). Thirty volunteers were recruited and asked to watch a series of short VR videos designed in three dimensions: time, action (characters), and space. Electroencephalograms (EEG) were recorded simultaneously during their participation. Subjective results show that any of the editing methods used would lead to an increased load and reduced immersion. Furthermore, the cognition of event segmentation theory also plays an instructive role in VR editing, with differences mainly focusing on frontal, parietal, and central regions. On this basis, visual evoked potential (VEP) analysis was performed, and the standardized low-resolution brain electromagnetic tomography algorithm (sLORETA) traceability method was used to analyze the data. The results of the VEP analysis suggest that shearing usually elicits a late event-related potential component, while the sources of VEP are mainly the frontal and parietal lobes. The insights derived from this work can be used as guidance for VR content creation, allowing VR image editing to reveal greater richness and unique beauty. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
Selection of Essential Neural Activity Timesteps for Intracortical Brain–Computer Interface Based on Recurrent Neural Network
Sensors 2021, 21(19), 6372; https://doi.org/10.3390/s21196372 - 24 Sep 2021
Viewed by 771
Abstract
Intracortical brain–computer interfaces (iBCIs) translate neural activity into control commands, thereby allowing paralyzed persons to control devices via their brain signals. Recurrent neural networks (RNNs) are widely used as neural decoders because they can learn neural response dynamics from continuous neural activity. Nevertheless, [...] Read more.
Intracortical brain–computer interfaces (iBCIs) translate neural activity into control commands, thereby allowing paralyzed persons to control devices via their brain signals. Recurrent neural networks (RNNs) are widely used as neural decoders because they can learn neural response dynamics from continuous neural activity. Nevertheless, excessively long or short input neural activity for an RNN may decrease its decoding performance. Based on the temporal attention module exploiting relations in features over time, we propose a temporal attention-aware timestep selection (TTS) method that improves the interpretability of the salience of each timestep in an input neural activity. Furthermore, TTS determines the appropriate input neural activity length for accurate neural decoding. Experimental results show that the proposed TTS efficiently selects 28 essential timesteps for RNN-based neural decoders, outperforming state-of-the-art neural decoders on two nonhuman primate datasets (R2=0.76±0.05 for monkey Indy and CC=0.91±0.01 for monkey N). In addition, it reduces the computation time for offline training (reducing 5–12%) and online prediction (reducing 16–18%). When visualizing the attention mechanism in TTS, the preparatory neural activity is consecutively highlighted during arm movement, and the most recent neural activity is highlighted during the resting state in nonhuman primates. Selecting only a few essential timesteps for an RNN-based neural decoder provides sufficient decoding performance and requires only a short computation time. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation
Sensors 2021, 21(9), 2953; https://doi.org/10.3390/s21092953 - 23 Apr 2021
Cited by 4 | Viewed by 1198
Abstract
Objectives: This paper aims to validate the performance and physical design of a wearable, unobtrusive ear-centered electroencephalography (EEG) device, dubbed “EARtrodes”, using early and late auditory evoked responses. Results would also offer a proof-of-concept for the device to be used as a concealed [...] Read more.
Objectives: This paper aims to validate the performance and physical design of a wearable, unobtrusive ear-centered electroencephalography (EEG) device, dubbed “EARtrodes”, using early and late auditory evoked responses. Results would also offer a proof-of-concept for the device to be used as a concealed brain–computer interface (BCI). Design: The device is composed of a custom-fitted earpiece and an ergonomic behind-the-ear piece with embedded electrodes made of a soft and flexible combination of silicone rubber and carbon fibers. The location of the conductive silicone electrodes inside the ear canal and the optimal geometry of the behind-the-ear piece were obtained through morphological and geometrical analysis of the human ear canal and the region around-the-ear. An entirely conductive generic earpiece was also developed to assess the potential of a universal, more affordable solution. Results: Early latency results illustrate the conductive silicone electrodes’ capability to record quality EEG signals, comparable to those obtained with traditional gold-plated electrodes. Additionally, late latency results demonstrate EARtrodes’ capacity to reliably detect decision-making processes from the ear. Conclusions: EEG results validate the performance of EARtrodes as a circum-aural and intra-aural EEG recording system adapted for a wide range of applications in audiology, neuroscience, clinical research, and as an unobtrusive BCI. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
Virtual Reality Customized 360-Degree Experiences for Stress Relief
Sensors 2021, 21(6), 2219; https://doi.org/10.3390/s21062219 - 22 Mar 2021
Cited by 5 | Viewed by 1538
Abstract
The latest studies in virtual reality (VR) have evidenced the potential of this technology to reproduce environments from multiple domains in an immersive way. For instance, in stress relief research, VR has been presented as a portable and inexpensive alternative to chromotherapy rooms, [...] Read more.
The latest studies in virtual reality (VR) have evidenced the potential of this technology to reproduce environments from multiple domains in an immersive way. For instance, in stress relief research, VR has been presented as a portable and inexpensive alternative to chromotherapy rooms, which require an adapted space and are expensive. In this work, we propose a portable and versatile alternative to the traditional chromotherapy color-loop treatment through four different 360-degree virtual experiences. A group of 23 healthy participants (mean age 22.65 ± 5.48) were conducted through a single-session experience divided into four phases while their electroencephalography (EEG) was recorded. First, they were stressed via the Montreal imaging stress task (MIST), and then relaxed using our VR proposal. We applied the Wilcoxon test to evaluate the relaxation effect in terms of the EEG relative gamma and self-perceived stress surveys. The results that we obtained validate the effectiveness of our 360-degree proposal to significantly reduce stress (p-value = 0.0001). Furthermore, the participants deemed our proposal comfortable and immersive (score above 3.5 out of 5). These results suggest that 360-degree VR experiences can mitigate stress, reduce costs, and bring stress relief assistance closer to the general public, like in workplaces or homes. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms
Sensors 2021, 21(6), 2096; https://doi.org/10.3390/s21062096 - 17 Mar 2021
Cited by 3 | Viewed by 1480
Abstract
Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution [...] Read more.
Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architectures include a vast number of hyperparameters on which the performance of the model relies. In this paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, but also their structure, which is able to propose solutions that consist of different architectures due to different layer combinations. The experimental results corroborate that deep architectures optimized by our method outperform the baseline approaches and result in computationally efficient models. Moreover, we demonstrate that optimized architectures improve the energy efficiency with respect to the baseline models. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning
Sensors 2021, 21(5), 1613; https://doi.org/10.3390/s21051613 - 25 Feb 2021
Cited by 8 | Viewed by 1652
Abstract
In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause [...] Read more.
In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
Virtual Reality as a Portable Alternative to Chromotherapy Rooms for Stress Relief: A Preliminary Study
Sensors 2020, 20(21), 6211; https://doi.org/10.3390/s20216211 - 30 Oct 2020
Cited by 8 | Viewed by 1953
Abstract
Chromotherapy rooms are comfortable spaces, used in places like special needs schools, where stimuli are carefully selected to cope with stress. However, these rooms are expensive and require a space that cannot be reutilized. In this article, we propose the use of virtual [...] Read more.
Chromotherapy rooms are comfortable spaces, used in places like special needs schools, where stimuli are carefully selected to cope with stress. However, these rooms are expensive and require a space that cannot be reutilized. In this article, we propose the use of virtual reality (VR) as an inexpensive and portable alternative to chromotherapy rooms for stress relief. We recreated a chromotherapy room stress relief program using a commercial head mounted display (HD). We assessed the stress level of two groups (test and control) through an EEG biomarker, the relative gamma, while they experienced a relaxation session. First, participants were stressed using the Montreal imaging stress task (MIST). Then, for relaxing, the control group utilized a chromotherapy room while the test group used virtual reality. We performed a hypothesis test to compare the self- perceived stress level at different stages of the experiment and it yielded no significant differences in reducing stress for both groups, during relaxing (p-value: 0.8379, α = 0.05) or any other block. Furthermore, according to participant surveys, the use of virtual reality was deemed immersive, comfortable and pleasant (3.9 out of 5). Our preliminary results validate our approach as an inexpensive and portable alternative to chromotherapy rooms for stress relief. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Article
Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG
Sensors 2020, 20(16), 4629; https://doi.org/10.3390/s20164629 - 17 Aug 2020
Cited by 20 | Viewed by 2264
Abstract
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide [...] Read more.
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Review

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Review
Past, Present, and Future of EEG-Based BCI Applications
Sensors 2022, 22(9), 3331; https://doi.org/10.3390/s22093331 - 26 Apr 2022
Viewed by 1015
Abstract
An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal [...] Read more.
An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Review
Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review
Sensors 2021, 21(18), 6285; https://doi.org/10.3390/s21186285 - 19 Sep 2021
Cited by 8 | Viewed by 1688
Abstract
The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges [...] Read more.
The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Other

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Perspective
CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain
Sensors 2021, 21(22), 7605; https://doi.org/10.3390/s21227605 - 16 Nov 2021
Cited by 3 | Viewed by 722
Abstract
The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of [...] Read more.
The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term ‘CyberEye’ to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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Systematic Review
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 - 12 Jul 2021
Cited by 13 | Viewed by 2150
Abstract
Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative [...] Read more.
Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive. Full article
(This article belongs to the Special Issue Brain–Computer Interfaces: Advances and Challenges)
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