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EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements
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EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2836

Special Issue Editor

Dr. Imène Jraidi

E-Mail Website
Guest Editor
Department of Educational and Counselling Psychology, McGill University, Montréal, QC H3A 1Y2, Canada
Interests: artificial intelligence; human and machine learning; multimodal interaction; cognitive and affective modeling

Special Issue Information

Dear Colleagues,

In recent years, the development of the brain–computer interface (BCI) technology has enhanced the ability of human brain activity to interact with the environment. It can further lead to the generation of new neurorehabilitation methods for people with physical disabilities (such as paralyzed patients and amputees) and brain injuries (such as stroke patients).

The development of artificial intelligence has promoted the advancement of electroencephalographic (EEG)-based BCI technologies. The intelligent brain–computer interface system based on EEG can continuously monitor the fluctuations in the human cognitive state under monotonous tasks, which is of great significance for both people requiring medical support and researchers. Currently, many BCI studies focus on EEG signals related to whole-body kinematics motor imagery and various senses. Therefore, it is necessary to study the various experimental paradigms used in EEG-based brain–computer interface systems.

This Special Issue will focus on the latest research progress in using EEG-based BCI. Researchers are welcome to submit original research on common brain–computer interface paradigms, signal processing methods, and their applications in target patients.

Dr. Imène Jraidi
Guest Editor

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.

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Published Papers (2 papers)

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Research

15 pages, 1937 KiB  
Article
Improving the Performance of Electrotactile Brain–Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials
by Marija Novičić, Olivera Djordjević, Vera Miler-Jerković, Ljubica Konstantinović and Andrej M. Savić
Sensors 2024, 24(24), 8048; https://doi.org/10.3390/s24248048 - 17 Dec 2024
Viewed by 844
Abstract
Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI [...] Read more.
Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value. Full article
(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)
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19 pages, 9860 KiB  
Article
High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain–Computer Interfaces
by Qingyu Sun, Shaojie Zhang, Guoya Dong, Weihua Pei, Xiaorong Gao and Yijun Wang
Sensors 2024, 24(11), 3521; https://doi.org/10.3390/s24113521 - 30 May 2024
Cited by 1 | Viewed by 1308
Abstract
In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain–computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density [...] Read more.
In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain–computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli. Full article
(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)
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