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45 pages, 10039 KiB  
Article
Design of an Interactive System by Combining Affective Computing Technology with Music for Stress Relief
by Chao-Ming Wang and Ching-Hsuan Lin
Electronics 2025, 14(15), 3087; https://doi.org/10.3390/electronics14153087 - 1 Aug 2025
Viewed by 179
Abstract
In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music [...] Read more.
In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music listening, emotion detection, and interactive devices. A prototype was created accordingly and refined through interviews with four experts and eleven users participating in a preliminary experiment. The system is grounded in a four-stage guided imagery and music framework, along with a static activity model focused on relaxation-based stress management. Emotion detection was achieved using a wearable EEG device (NeuroSky’s MindWave Mobile device) and a two-dimensional emotion model, and the emotional states were translated into visual representations using seasonal and weather metaphors. A formal experiment involving 52 users was conducted. The system was evaluated, and its effectiveness confirmed, through user interviews and questionnaire surveys, with statistical analysis conducted using SPSS 26 and AMOS 23. The findings reveal that: (1) integrating emotion sensing with music listening creates a novel and engaging interactive experience; (2) emotional states can be effectively visualized using nature-inspired metaphors, enhancing user immersion and understanding; and (3) the combination of music listening, guided imagery, and real-time emotional feedback successfully promotes emotional relaxation and increases self-awareness. Full article
(This article belongs to the Special Issue New Trends in Human-Computer Interactions for Smart Devices)
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33 pages, 8161 KiB  
Article
Comparison of LSTM- and GRU-Type RNN Networks for Attention and Meditation Prediction on Raw EEG Data from Low-Cost Headsets
by Fernando Rivas, Jesús Enrique Sierra-Garcia and Jose María Camara
Electronics 2025, 14(4), 707; https://doi.org/10.3390/electronics14040707 - 12 Feb 2025
Cited by 4 | Viewed by 4674
Abstract
This study bridges neuroscience and artificial intelligence by developing advanced models to predict cognitive states—specifically attention and meditation—using raw EEG data collected from low-cost commercial devices such as NeuroSky and Brainlink. Leveraging the temporal capabilities of recurrent neural networks (RNNs), particularly long short-term [...] Read more.
This study bridges neuroscience and artificial intelligence by developing advanced models to predict cognitive states—specifically attention and meditation—using raw EEG data collected from low-cost commercial devices such as NeuroSky and Brainlink. Leveraging the temporal capabilities of recurrent neural networks (RNNs), particularly long short-term memory (LSTM) and gated recurrent units (GRUs), the study evaluates their effectiveness in predicting future cognitive states. These predictions have applications in real-time brain–computer interface (BCI) systems, enhancing responsiveness and adaptability in dynamic environments like robotic control. The proposed LSTM model demonstrated superior predictive accuracy for meditation states, achieving a Root Mean Squared Error (RMSE) of 10.90, while the GRU model excelled in predicting attention states, with an RMSE of 11.79. Both models outperformed the results provided by the proprietary eSense algorithm, reinforcing the potential of raw EEG data in cognitive-state analysis. Notably, inference times were optimized to under 50 milliseconds, making the models suitable for real-time applications. These findings underline the feasibility of using raw EEG signals from affordable devices for robust real-time prediction, offering a significant step forward in applied neuroscience. This research lays the groundwork for further exploration of RNN architectures in BCI applications, enabling safer, more intuitive, and personalized interactions in assistive technologies and beyond. Full article
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54 pages, 14528 KiB  
Article
Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs
by Fernando Rivas, Jesús Enrique Sierra and Jose María Cámara
Electronics 2024, 13(6), 1013; https://doi.org/10.3390/electronics13061013 - 7 Mar 2024
Cited by 2 | Viewed by 3204
Abstract
Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore [...] Read more.
Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment. Full article
(This article belongs to the Special Issue Intelligent Control and Computing in Advanced Robotics)
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9 pages, 11181 KiB  
Proceeding Paper
Control of Unmanned Vehicles in Smart Cities Using a Multi-Modal Brain–Computer Interface
by Daniyar Wolf, Mark Mamchenko and Elena Jharko
Eng. Proc. 2023, 33(1), 43; https://doi.org/10.3390/engproc2023033043 - 28 Jun 2023
Cited by 1 | Viewed by 2994
Abstract
The article presents an overview of several studies in the field of Brain–Computer Interfaces (BCIs), the requirements for the architecture of such promising devices, as well as multi-modal BCI for drone control in a smart-city environment. Distinctive features of the proposed solution are [...] Read more.
The article presents an overview of several studies in the field of Brain–Computer Interfaces (BCIs), the requirements for the architecture of such promising devices, as well as multi-modal BCI for drone control in a smart-city environment. Distinctive features of the proposed solution are the simplicity of the architecture (the use of only one smartphone for both receiving and processing bio-signals from the headset and transmitting commands to the drone), an open-source software solution for signal processing, generating, and sending commands to the unmanned aerial vehicle (UAV), as well as multimodality of the BCI (the use of both electroencephalographic (EEG) and electrooculographic (EOG) signals of the operator). For bio-signal acquisition, we used the NeuroSky Mindwave Mobile 2 headset, which is connected to an Android-based smartphone via Bluetooth. The developed Android application (Tello NeuroSky) processes signals from the headset and generates and transmits commands to the DJI Tello UAV via Wi-Fi. The decrease (depression) and increase of α- and β-rhythms of the brain, as well as EOG signals that occur during blinking were the triggers for UAV commands. The developed software allows the manual setting of the minimum, maximum and threshold values for the processed bio-signals. The following commands for the UAV were implemented: take-off, landing, forward movement, and backwards movement. Two threads of the smartphone’s central processing unit (CPU) were utilized when processing signals in the software to increase the performance: for signal processing (1-D Daubechies 2 (db2) wavelet transform) and updating data on the diagrams, and for generating and transmitting commands to the drone. Full article
(This article belongs to the Proceedings of 15th International Conference “Intelligent Systems” (INTELS’22))
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15 pages, 2044 KiB  
Article
Physiological Effects of a Garden Plant Smellscape from the Perspective of Perceptual Interaction
by Xinguo Zhang, Jiayu Guo, Xiaowan Zhang and Qixiang Zhang
Int. J. Environ. Res. Public Health 2023, 20(6), 5004; https://doi.org/10.3390/ijerph20065004 - 12 Mar 2023
Cited by 10 | Viewed by 3009
Abstract
The purpose of this study was to investigate the physiological recovery effects of olfactory, visual and olfactory–visual stimuli associated with garden plants. In a randomized controlled study design, ninety-five Chinese university students were randomly selected to be exposed to stimulus materials, namely the [...] Read more.
The purpose of this study was to investigate the physiological recovery effects of olfactory, visual and olfactory–visual stimuli associated with garden plants. In a randomized controlled study design, ninety-five Chinese university students were randomly selected to be exposed to stimulus materials, namely the odor of Osmanthus fragrans and a corresponding panoramic image of a landscape featuring the plant. Physiological indexes were measured by the VISHEEW multiparameter biofeedback instrument and a NeuroSky EEG tester in a virtual simulation laboratory. The results showed the following: (1) In the olfactory stimulation group, from before to during exposure to the stimuli, the subjects’ diastolic blood pressure (DBP) (ΔDBP = 4.37 ± 1.69 mmHg, p < 0.05) and pulse pressure (PP) values increased (ΔPP = −4.56 ± 1.24 mmHg, p < 0.05), while their pulse (p) values decreased (ΔP = −2.34 ± 1.16 bmp, p < 0.05) significantly. When compared to the control group, only the amplitudes of α and β brainwaves increased significantly (Δα = 0.37 ± 2.09 µV, Δβ = 0.34 ± 1.01 µV, p < 0.05). (2) In the visual stimulation group, the amplitudes of skin conductance (SC) (ΔSC = 0.19 ± 0.01 µΩ, p < 0.05), α brainwaves (Δα = 6.2 ± 2.26 µV, p < 0.05) and β brainwaves (Δβ = 5.51 ± 1.7 µV, p < 0.05) all increased significantly relative to the control group. (3) In the olfactory–visual stimulus group, DBP (ΔDBP = 3.26 ± 0.45 mmHg, p < 0.05) values increased, and PP values decreased (ΔPP = −3.48 ± 0.33 bmp, p < 0.05) significantly from before to during exposure to the stimuli. The amplitudes of SC (ΔSC = 0.45 ± 0.34 µΩ, p < 0.05), α brainwaves (Δα = 2.28 ± 1.74 µV, p < 0.05) and β brainwaves (Δβ = 1.4 ± 0.52 µV, p < 0.05) all increased significantly relative to the control group. The results of this study show that the interaction of olfactory and visual stimuli associated with a garden plant odor landscape was able to relax and refresh the body to a certain extent, and this physiological health effect was greater with regards to the integrated response of the autonomic nervous system and central nervous system than the effect of only smelling or viewing the stimuli. In the planning and designing of plant smellscapes in garden green space, it should be ensured that plant odors and corresponding landscapes are present at the same time in order to ensure the best health effect. Full article
(This article belongs to the Topic Bioclimatic Designs to Enhance Urban/Rural Resilience)
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13 pages, 355 KiB  
Article
ASMR as Idiosyncratic Experience: Experimental Evidence
by Chiara Pedrini, Lorena Marotta and Andrea Guazzini
Int. J. Environ. Res. Public Health 2021, 18(21), 11459; https://doi.org/10.3390/ijerph182111459 - 30 Oct 2021
Cited by 11 | Viewed by 7316
Abstract
The Autonomous Sensory Meridian Response (ASMR) is a tingling sensation across the scalp that occur in response to specific triggering audio and visual stimuli, connected with the Default Mode Network. Our study (N = 76) aimed to test the neurophysiology of ASMR by [...] Read more.
The Autonomous Sensory Meridian Response (ASMR) is a tingling sensation across the scalp that occur in response to specific triggering audio and visual stimuli, connected with the Default Mode Network. Our study (N = 76) aimed to test the neurophysiology of ASMR by examining pupil diameter and brain activity. Assuming the idiosyncratic nature of ASMR, we expected results detecting opposite physiological outcomes considering pupil diameter and brain activation. We used a battery of self-reports to investigate psychological dimensions; for the physiological measures, we used two instruments: PupilCore and NeuroSky MindWave Mobile 2. The results showed an augmented pupillary diameter during the ASMR video, regardless of the perception of tingles. On the other hand, the arousal level during the ASMR video was lower than the other conditions. The difference between the two neurophysiological measures appeared as peculiar and can be considered as the promoting phenomenon for ASMR psychological outcomes. Full article
(This article belongs to the Special Issue Human Health Dynamics in the Mobile and Big Data Era)
11 pages, 1535 KiB  
Article
Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset
by Almudena Serrano-Barroso, Roma Siugzdaite, Jaime Guerrero-Cubero, Alberto J. Molina-Cantero, Isabel M. Gomez-Gonzalez, Juan Carlos Lopez and Juan Pedro Vargas
Sensors 2021, 21(9), 3221; https://doi.org/10.3390/s21093221 - 6 May 2021
Cited by 36 | Viewed by 7604
Abstract
Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to [...] Read more.
Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Attention changes registered with NeuroSky MindWave in combination with the CARAS-R psychological test were used to characterise the attentional profiles of 52 non-ADHD and 23 ADHD children aged 7 to 12 years old. The analyses revealed that the GokEvolution was valuable in measuring attention through its use of EEG–BCI technology. The ADHD group showed lower levels of attention and more variability in brain attentional responses when compared to the control group. The application was able to map the low attention profiles of the ADHD group when compared to the control group and could distinguish between participants who completed the task and those who did not. Therefore, this system could potentially be used in clinical settings as a screening tool for early detection of attentional traits in order to prevent their development. Full article
(This article belongs to the Section Electronic Sensors)
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26 pages, 403 KiB  
Review
Wireless Sensors for Brain Activity—A Survey
by Mahyar TajDini, Volodymyr Sokolov, Ievgeniia Kuzminykh, Stavros Shiaeles and Bogdan Ghita
Electronics 2020, 9(12), 2092; https://doi.org/10.3390/electronics9122092 - 8 Dec 2020
Cited by 32 | Viewed by 8066
Abstract
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex [...] Read more.
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation. Full article
(This article belongs to the Special Issue Advanced Technologies and Challenges in Brain Machine Interface)
23 pages, 4397 KiB  
Article
Neurodynamics of Patients during a Dolphin-Assisted Therapy by Means of a Fractal Intraneural Analysis
by Oswaldo Morales Matamoros, Jesús Jaime Moreno Escobar, Ricardo Tejeida Padilla and Ixchel Lina Reyes
Brain Sci. 2020, 10(6), 403; https://doi.org/10.3390/brainsci10060403 - 25 Jun 2020
Cited by 11 | Viewed by 4112
Abstract
The recent proliferation of sensor technology applications in therapies for children’s disabilities to promote positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-Assisted Therapy (DAT) has also become a topic of public [...] Read more.
The recent proliferation of sensor technology applications in therapies for children’s disabilities to promote positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-Assisted Therapy (DAT) has also become a topic of public and research interest for these disorders’ intervention and treatment. This work exposes the development of a system that controls brain–computer interaction when a patient with different abilities undergoes a DAT. To develop the proposed system, TGAM1, i.e., ThinkGear-AM1 series of NeuroSky company, was used, connecting it to an isolated Bluetooth 4.0 communication protocol from a brackish and humid environment, and a Notch Filter was applied to reduce the input noise. In this way, at Definiti Ixtapa-Mexico facilities, we explored the behavior of three children with Infantile Spastic Cerebral Palsy (Experiment 1), as well as the behavior of Obsessive Compulsive Disorder and neurotypic children (Experiment 2). This was done applying the Power Spectrum Density (PSD) and the Self-Affine Analysis (SSA) from Electroencephalogram (EEG) biosignals. The EEG Raw data were time series showing the cerebral brain activity (voltage versus time) before and during DAT for the Experiment 1, and before, during DAT and after for the Experiment 2. Likewise, the EEW RAW data were recorded by the first frontopolar electrode (FP1) by means of an EEG biosensor TGAM1 Module. From the PSD we found that in all child patients a huge increment of brain activity during DAT regarding the before and after therapy periods around 376.28%. Moreover, from the SSA we found that the structure function of the all five child patients displayed an antipersistent behavior, characterized by σ δ t H , for before, during DAT and after. Nonetheless, we propose that one way to assess whether a DAT is being efficient to the child patients is to increase the during DAT time when the samples are collected, supposing the data fitting by a power law will raise the time, displaying a persistent behavior or positive correlations, until a crossover appears and the curve tends to be horizontal, pointing out that our system has reached a stationary state. Full article
(This article belongs to the Special Issue Behavioral and Cognitive Neurodynamics)
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18 pages, 4480 KiB  
Article
Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study
by Héctor Rieiro, Carolina Diaz-Piedra, José Miguel Morales, Andrés Catena, Samuel Romero, Joaquin Roca-Gonzalez, Luis J. Fuentes and Leandro L. Di Stasi
Sensors 2019, 19(12), 2808; https://doi.org/10.3390/s19122808 - 23 Jun 2019
Cited by 45 | Viewed by 9757
Abstract
The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a [...] Read more.
The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies. Full article
(This article belongs to the Collection Wearable and Unobtrusive Biomedical Monitoring)
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