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Electronics
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EEG Analysis and Brain–Computer Interface (BCI) Technology
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EEG Analysis and Brain–Computer Interface (BCI) Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 11296

Special Issue Editors

Prof. Dr. Zhao Lv

E-Mail Website
Guest Editor
School of Computer Science and Technology, Anhui University, Hefei 230601, China
Interests: EGG
Dr. Yongjun Zheng

E-Mail Website
Guest Editor
School of Physics, Engineering and Computer Science, College Lane Campus, University of Hertfordshire, Hertfordshire AL10 9AB , UK
Interests: human robotic interaction; AR/VR; data visualization
Dr. Chao Zhang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Anhui University, Hefei 230601, China
Interests: multimodal biomedical signal processing; iPPG

Special Issue Information

Dear Colleagues,

Brain–computer interface (BCI) plays an important role in intelligent interaction systems, which refers to the direct communication link between the brain and external types of equipment to realize information exchange. As one of the most important research fields in intelligence science, BCI has acquired great improvements and potential applications in various fields such as rehabilitation, affective computing, neuroscience, robotics, and gaming.

The aim of this Special Issue is to present advanced research in the field of BCI, and to highlight major open questions to address the outstanding challenges in EEG signal analysis as well as BCI technology. Papers that address innovative applications and algorithms related to EEG analysis and BCI technology are welcome. This Special Issue welcomes submissions of original research and review articles, along with data reports, hypothesis and theory, methods, mini reviews, and study protocol. Topics of interest include, but are not limited to, the following:

  • BCI paradigm including MI, SSVEP, P300, etc;
  • EEG signals analysis;
  • EEG-based affective computing;
  • EEG-based auditory attention decoding;
  • EEG-based neuroimaging and neural mechanism;
  • Other brain–computer interface technologies.

Prof. Dr. Zhao Lv
Dr. Yongjun Zheng
Dr. Chao Zhang
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. Electronics 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

  • EEG
  • brain–computer interface
  • signal analysis
  • BCI applications

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Published Papers (5 papers)

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Research

18 pages, 2663 KiB  
Article
Brain-Computer Interface Based Engagement Feedback in Virtual Reality Rehabilitation: Promoting Motor Cortex Activation
by Hyunmi Lim, Bilal Ahmed and Jeonghun Ku
Electronics 2025, 14(5), 827; https://doi.org/10.3390/electronics14050827 - 20 Feb 2025
Viewed by 791
Abstract
Maintaining optimal levels of engagement during rehabilitation training is crucial for inducing neuroplasticity in the motor cortex, which directly influences positive rehabilitation outcomes. In this research article, we propose a virtual reality (VR) rehabilitation system that incorporates a steady-state visual evoked potential (SSVEP) [...] Read more.
Maintaining optimal levels of engagement during rehabilitation training is crucial for inducing neuroplasticity in the motor cortex, which directly influences positive rehabilitation outcomes. In this research article, we propose a virtual reality (VR) rehabilitation system that incorporates a steady-state visual evoked potential (SSVEP) paradigm to provide engagement feedback. The system utilizes a flickering target and cursor to detect the user’s engagement levels during a target-tracking task. Eighteen healthy participants were recruited to experience three experimental conditions: no feedback (NoF), performance feedback (PF), and neurofeedback (NF). Our results reveal significantly greater Mu suppression in the NF condition compared to the other conditions. However, no significant differences were observed in performance metrics, such as tracking error, among the three conditions. The amount of feedback between the PF and NF conditions also showed no substantial difference. These findings suggest the efficacy of our SSVEP-based engagement feedback paradigm in stimulating motor cortex activity during rehabilitation. Consequently, we conclude that neurofeedback, based on the user’s attentional state, proves to be more effective in promoting motor cortex activation and facilitating neuroplastic changes. This research highlights the potential of integrating VR rehabilitation with an engagement feedback system for successful rehabilitation training. Full article
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)
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19 pages, 3167 KiB  
Article
An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey
by Jiaxuan Wu and Jingjing Wang
Electronics 2024, 13(14), 2767; https://doi.org/10.3390/electronics13142767 - 14 Jul 2024
Cited by 1 | Viewed by 2037
Abstract
The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical [...] Read more.
The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms. Full article
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)
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18 pages, 2102 KiB  
Article
A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning
by Shiqi Liu, Yuting Zhou, Xuemei Yang, Xiaoying Wang and Junping Yin
Electronics 2024, 13(14), 2727; https://doi.org/10.3390/electronics13142727 - 11 Jul 2024
Cited by 6 | Viewed by 2276
Abstract
Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become [...] Read more.
Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach. Full article
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)
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15 pages, 3626 KiB  
Article
Brain–Computer Interface Based on PLV-Spatial Filter and LSTM Classification for Intuitive Control of Avatars
by Kevin Martín-Chinea, José Francisco Gómez-González and Leopoldo Acosta
Electronics 2024, 13(11), 2088; https://doi.org/10.3390/electronics13112088 - 27 May 2024
Viewed by 1098
Abstract
This study researches the combination of the brain–computer interface (BCI) and virtual reality (VR) in order to improve user experience and facilitate control learning in a safe environment. In addition, it assesses the applicability of the phase-locking value spatial filtering (PLV-SF) method and [...] Read more.
This study researches the combination of the brain–computer interface (BCI) and virtual reality (VR) in order to improve user experience and facilitate control learning in a safe environment. In addition, it assesses the applicability of the phase-locking value spatial filtering (PLV-SF) method and the Short-Term Memory Network (LSTM) in a real-time EEG-based BCI. The PLV-SF has been shown to improve signal quality, and the LSTM exhibits more stable and accurate behavior. Ten healthy volunteers, six men and four women aged 22 to 37 years, participated in tasks inside a virtual house, using their EEG states to direct their movements and actions through a commercial, low-cost wireless EEG device together with a virtual reality system. A BCI and VR can be used effectively to enable the intuitive control of virtual environments by immersing users in real-life situations, making the experience engaging, fun, and safe. Control test times decreased significantly from 3.65 min and 7.79 min in the first and second quartiles, respectively, to 2.56 min and 4.28 min. In addition, a free route was performed for the three best volunteers who finished in an average time of 6.30 min. Full article
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)
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14 pages, 1513 KiB  
Article
A Fine-Grained Approach for EEG-Based Emotion Recognition Using Clustering and Hybrid Deep Neural Networks
by Liumei Zhang, Bowen Xia, Yichuan Wang, Wei Zhang and Yu Han
Electronics 2023, 12(23), 4717; https://doi.org/10.3390/electronics12234717 - 21 Nov 2023
Cited by 10 | Viewed by 3400
Abstract
Emotion recognition, as an important part of human-computer interaction, is of great research significance and has already played a role in the fields of artificial intelligence, healthcare, and distance education. In recent times, there has been a growing trend in using deep learning [...] Read more.
Emotion recognition, as an important part of human-computer interaction, is of great research significance and has already played a role in the fields of artificial intelligence, healthcare, and distance education. In recent times, there has been a growing trend in using deep learning techniques for EEG emotion recognition. These methods have shown higher accuracy in recognizing emotions when compared with traditional machine learning methods. However, most of the current EEG emotion recognition performs multi-category single-label prediction, and is a binary classification problem based on the dimensional model. This simplifies the fact that human emotions are mixed and complex. In order to adapt to real-world applications, fine-grained emotion recognition is necessary. We propose a new method for building emotion classification labels using linguistic resource and density-based spatial clustering of applications with noise (DBSCAN). Additionally, we integrate the frequency domain and spatial features of emotional EEG signals and feed these features into a serial network that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network (RNN) for EEG emotion feature learning and classification. We conduct emotion classification experiments on the DEAP dataset, and the results show that our method has an average emotion classification accuracy of 92.98% per subject, validating the effectiveness of the improvements we have made to our emotion classification method. Our method for emotion classification holds potential for future use in the domain of affective computing, such as mental health care, education, social media, and so on. By constructing an automatic emotion analysis system using our method to enable the machine to understand the emotional implications conveyed by the subjects’ EEG signals, it can provide healthcare professionals with valuable information for effective treatment outcomes. Full article
(This article belongs to the Special Issue EEG Analysis and Brain–Computer Interface (BCI) Technology)
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