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

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

Deadline for manuscript submissions: closed (15 October 2025) | Viewed by 10577

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Dr. Imène Jraidi

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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
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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

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Published Papers (7 papers)

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Research

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23 pages, 6005 KB

Open AccessArticle

Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification

by Alejandra Gomez-Rivera, Andrés M. Álvarez-Meza, David Cárdenas-Peña and Alvaro Orozco-Gutierrez

Sensors 2025, 25(22), 7067; https://doi.org/10.3390/s25227067 - 19 Nov 2025

Viewed by 380

Abstract

Reliable decoding of motor imagery (MI) from electroencephalographic signals remains a challenging problem due to their nonlinear, noisy, and non-stationary nature. To address this issue, this work proposes an end-to-end deep learning model, termed TEKTE-Net, that integrates time embeddings with a kernelized Transfer Entropy estimator to infer directed functional connectivity in MI-based brain–computer interface (BCI) systems. The proposed model incorporates a customized convolutional module that performs Takens’ embedding, enabling the decoding of the underlying EEG activity without requiring explicit preprocessing. Further, the architecture estimates nonlinear and time-delayed interactions between cortical regions using Rational Quadratic kernels within a differentiable framework. Evaluation of TEKTE-Net on semi-synthetic causal benchmarks and the BCI Competition IV 2a dataset demonstrates robustness to low signal-to-noise conditions and interpretability through temporal, spatial, and spectral analyses of learned connectivity patterns. In particular, the model automatically highlights contralateral activations during MI and promotes spectral selectivity for the beta and gamma bands. Overall, TEKTE-Net offers a fully trainable estimator of functional brain connectivity for decoding EEG activity, supporting MI-BCI applications, and promoting interpretability of deep learning models. Full article

(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)

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25 pages, 5973 KB

Open AccessArticle

An Attention-Residual Convolutional Network for Real-Time Seizure Classification on Edge Devices

by Peter A. Akor, Godwin Enemali, Usman Muhammad, Rajiv Ranjan Singh and Hadi Larijani

Sensors 2025, 25(22), 6855; https://doi.org/10.3390/s25226855 - 10 Nov 2025

Viewed by 553

Abstract

Epilepsy affects over 50 million people globally, with accurate seizure type classification directly influencing treatment selection as different seizure types respond to specific antiepileptic medications. Manual electroencephalogram (EEG) interpretation remains time-intensive and requires specialized expertise, creating clinical workflow bottlenecks. This work presents EEG-ARCNet, an attention-residual convolutional network integrating residual connections with channel attention mechanisms to extract discriminative temporal and spectral features from multi-channel EEG recordings. The model combines nine statistical temporal features with five frequency-band power measures through Welch’s spectral decomposition, processed through attention-enhanced convolutional pathways. Evaluated on the Temple University Hospital Seizure Corpus, EEG-ARCNet achieved 99.65% accuracy with 99.59% macro-averaged F1-score across five seizure types (absence, focal non-specific, simple partial, tonic-clonic, and tonic). To validate practical deployment, the model was implemented on Raspberry Pi 4, achieving a 2.06 ms average inference time per 10 s segment with 35.4% CPU utilization and 499.4 MB memory consumption. The combination of high classification accuracy and efficient edge deployment demonstrates technical feasibility for resource-constrained seizure-monitoring applications. Full article

(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)

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29 pages, 3490 KB

Open AccessArticle

Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based Pattern Detection

by Sonia Rocío Moreno-Castelblanco, Manuel Andrés Vélez-Guerrero and Mauro Callejas-Cuervo

Sensors 2025, 25(20), 6387; https://doi.org/10.3390/s25206387 - 16 Oct 2025

Viewed by 912

Abstract

Advances in brain–computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky–Golay filtering with a Random Forest classifier, reaching 87.36% ± 4% accuracy and an F1-score of 87.18% ± 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments. Full article

(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)

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21 pages, 2248 KB

Open AccessArticle

TSFNet: Temporal-Spatial Fusion Network for Hybrid Brain-Computer Interface

by Yan Zhang, Bo Yin and Xiaoyang Yuan

Sensors 2025, 25(19), 6111; https://doi.org/10.3390/s25196111 - 3 Oct 2025

Viewed by 973

Abstract

Unimodal brain–computer interfaces (BCIs) often suffer from inherent limitations due to the characteristic of using single modalities. While hybrid BCIs combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer complementary advantages, effectively integrating their spatiotemporal features remains a challenge due to inherent signal asynchrony. This study aims to develop a novel deep fusion network to achieve synergistic integration of EEG and fNIRS signals for improved classification performance across different tasks. We propose a novel Temporal-Spatial Fusion Network (TSFNet), which consists of two key sublayers: the EEG-fNIRS-guided Fusion (EFGF) layer and the Cross-Attention-based Feature Enhancement (CAFÉ) layer. The EFGF layer extracts temporal features from EEG and spatial features from fNIRS to generate a hybrid attention map, which is utilized to achieve more effective and complementary integration of spatiotemporal information. The CAFÉ layer enables bidirectional interaction between fNIRS and fusion features via a cross-attention mechanism, which enhances the fusion features and selectively filters informative fNIRS representations. Through the two sublayers, TSFNet achieves deep fusion of multimodal features. Finally, TSFNet is evaluated on motor imagery (MI), mental arithmetic (MA), and word generation (WG) classification tasks. Experimental results demonstrate that TSFNet achieves superior classification performance, with average accuracies of 70.18% for MI, 86.26% for MA, and 81.13% for WG, outperforming existing state-of-the-art multimodal algorithms. These findings suggest that TSFNet provides an effective solution for spatiotemporal feature fusion in hybrid BCIs, with potential applications in real-world BCI systems. Full article

(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)

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15 pages, 1937 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1459

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 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 KB

Open AccessArticle

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 5 | Viewed by 2075

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 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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Review

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29 pages, 1397 KB

Open AccessReview

Artificial Intelligence Approaches for EEG Signal Acquisition and Processing in Lower-Limb Motor Imagery: A Systematic Review

by Sonia Rocío Moreno-Castelblanco, Manuel Andrés Vélez-Guerrero and Mauro Callejas-Cuervo

Sensors 2025, 25(16), 5030; https://doi.org/10.3390/s25165030 - 13 Aug 2025

Cited by 1 | Viewed by 2805

Abstract

Background: Motor imagery (MI) is defined as the cognitive ability to simulate motor movements while suppressing muscular activity. The electroencephalographic (EEG) signals associated with lower limb MI have become essential in brain–computer interface (BCI) research aimed at assisting individuals with motor disabilities. Objective: This systematic review aims to evaluate methodologies for acquiring and processing EEG signals within brain–computer interface (BCI) applications to accurately identify lower limb MI. Methods: A systematic search in Scopus and IEEE Xplore identified 287 records on EEG-based lower-limb MI using artificial intelligence. Following PRISMA guidelines (non-registered), 35 studies met the inclusion criteria after screening and full-text review. Results: Among the selected studies, 85% applied machine or deep learning classifiers such as SVM, CNN, and LSTM, while 65% incorporated multimodal fusion strategies, and 50% implemented decomposition algorithms. These methods improved classification accuracy, signal interpretability, and real-time application potential. Nonetheless, methodological variability and a lack of standardization persist across studies, posing barriers to clinical implementation. Conclusions: AI-based EEG analysis effectively decodes lower-limb motor imagery. Future efforts should focus on harmonizing methods, standardizing datasets, and developing portable systems to improve neurorehabilitation outcomes. This review provides a foundation for advancing MI-based BCIs. Full article

(This article belongs to the Special Issue EEG-Based Brain–Computer Interface: Trends, Challenges and Advancements)

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