Dual-Branch Convolution Network With Efficient Channel Attention for EEG-Based Motor Imagery Classification DOI Creative Commons
Kai Zhou,

Aierken Haimudula,

Wanying Tang

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 74930 - 74943

Опубликована: Янв. 1, 2024

Brain-Computer Interface (BCI) is a revolutionary technique that employs wearable electroencephalography (EEG) sensors and artificial intelligence (AI) to monitor decode brain activity. EEG-based motor imagery (MI) signal widely utilized in various BCI fields including intelligent healthcare, robot control, smart homes. Yet, the limited capability of decoding signals remains significant obstacle techniques expansion. In this study, we describe an architecture known as dual-branch attention temporal convolutional network (DB-ATCNet) for MI classification. DB-ATCNet improves classification performance with relatively fewer parameters by utilizing channel attention. The model consists two primary modules: convolution (ADBC) fusion (ATFC). ADBC module utilizes extract low-level MI-EEG features incorporates improve spatial feature extraction. ATFC sliding windows self-attention obtain high-level features, strategies minimize information loss. achieved subject-independent accuracies 87.33% 69.58% two-class four-class tasks, respectively, on PhysioNet dataset. On Competition IV-2a dataset, it accuracy 71.34% 87.54% subject-dependent evaluations, surpassing existing methods. code available at https://github.com/zk-xju/DB-ATCNet.

Язык: Английский

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques DOI Creative Commons
Ahmad Chaddad, Yihang Wu,

Reem Kateb

и другие.

Sensors, Год журнала: 2023, Номер 23(14), С. 6434 - 6434

Опубликована: Июль 16, 2023

The electroencephalography (EEG) signal is a noninvasive and complex that has numerous applications in biomedical fields, including sleep the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing feature extraction methods to analyze EEG signals. In this study, we comprehensive review of articles related processing. We searched major scientific engineering databases summarized results our findings. Our survey encompassed entire process processing, from acquisition pretreatment (denoising) extraction, classification, application. present detailed discussion comparison various techniques used for Additionally, identify current limitations these their future development trends. conclude by offering some suggestions research field

Язык: Английский

Процитировано

105
Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications DOI Creative Commons
Berdakh Abibullaev, Aigerim Keutayeva, Amin Zollanvari

и другие.

IEEE Access, Год журнала: 2023, Номер 11, С. 127271 - 127301

Опубликована: Янв. 1, 2023

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years. The integration of deep learning techniques, specifically transformers, has shown promising development research and application domains. Transformers, which were originally designed for natural language processing, now made notable inroads into BCIs, offering a unique self-attention mechanism that adeptly handles the temporal dynamics brain signals. This comprehensive survey delves transformers providing readers with lucid understanding their foundational principles, inherent advantages, potential challenges, diverse applications. In addition to discussing benefits we also address limitations, such as computational overhead, interpretability concerns, data-intensive nature these models, well-rounded analysis. Furthermore, paper sheds light on myriad BCI applications benefited from incorporation transformers. These span motor imagery decoding, emotion recognition, sleep stage analysis novel ventures speech reconstruction. review serves holistic guide researchers practitioners, panoramic view transformative landscape. With inclusion examples references, will gain deeper topic its significance field.

Язык: Английский

Процитировано

32
Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model DOI
Shiqi Yu, Zedong Wang, Fei Wang

и другие.

Cerebral Cortex, Год журнала: 2024, Номер 34(2)

Опубликована: Янв. 5, 2024

Abstract Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses specific movement without physically executing it. Recently, MI-based brain–computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding neural mechanisms still face huge challenges. These seriously hinder the clinical application development BCI systems based on MI. Thus, it very necessary to develop new methods decode tasks. In this work, we propose multi-branch convolutional network (MBCNN) with temporal (TCN), end-to-end deep learning framework multi-class We first used MBCNN capture electroencephalography signals information spectral domains through different kernels. Then, introduce TCN extract more discriminative features. The within-subject cross-session strategy validate classification performance dataset Competition IV-2a. results showed that achieved 75.08% average accuracy for 4-class task classification, outperforming several state-of-the-art approaches. proposed MBCNN-TCN-Net successfully captures features decodes tasks effectively, improving MI-BCIs. Our findings could provide significant potential systems.

Язык: Английский

Процитировано

10
Multi-branch spatial-temporal-spectral convolutional neural networks for multi-task motor imagery EEG classification DOI

Zikun Cai,

Tian-jian Luo, Xuan Cao

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 93, С. 106156 - 106156

Опубликована: Фев. 28, 2024

Язык: Английский

Процитировано

9
MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment DOI
Subhayu Dutta,

Saptiva Goswami,

Sonali Debnath

и другие.

Computers in Biology and Medicine, Год журнала: 2025, Номер 188, С. 109795 - 109795

Опубликована: Фев. 12, 2025

Язык: Английский

Процитировано

1
An EEG-based cross-subject interpretable CNN for game player expertise level classification DOI

Liqi Lin,

Pengrui Li, Q. Wang

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121658 - 121658

Опубликована: Сен. 22, 2023

Язык: Английский

Процитировано

16
EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms DOI Creative Commons
Martin Wimpff, Leonardo Gizzi, Jan Zerfowski

и другие.

Journal of Neural Engineering, Год журнала: 2024, Номер 21(3), С. 036020 - 036020

Опубликована: Май 8, 2024

The objective of this study is to investigate the application various channel attention mechanisms within domain brain-computer interface (BCI) for motor imagery decoding. Channel can be seen as a powerful evolution spatial filters traditionally used This systematically compares such by integrating them into lightweight architecture framework evaluate their impact.

Язык: Английский

Процитировано

6
A time-frequency map generation network embedded with spectral self-attention for motor imagery classification DOI
Xu Niu, Na Lü, Ruofan Yan

и другие.

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 93, С. 106206 - 106206

Опубликована: Март 16, 2024

Язык: Английский

Процитировано

3
EEGGAN-Net: enhancing EEG signal classification through data augmentation DOI Creative Commons
Jiuxiang Song, Qiang Zhai, Chuang Wang

и другие.

Frontiers in Human Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Июнь 21, 2024

Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, constrained accuracy electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications.

Язык: Английский

Процитировано

3
EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery DOI Creative Commons
Chengcheng Fan, Banghua Yang, Xiaoou Li

и другие.

Journal of Integrative Neuroscience, Год журнала: 2024, Номер 23(8)

Опубликована: Авг. 20, 2024

Background: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. effective extraction features is vital due to the variability among individuals and temporal states. Methods: This study introduces a novel network architecture, 3D-convolutional network-generative adversarial (3D-CNN-GAN), both within-session cross-session imagery. Initially, EEG signals were extracted over various time intervals using sliding window technique, capturing temporal, frequency, phase construct temporal-frequency-phase feature (TFPF) three-dimensional map. Generative (GANs) then employed synthesize artificial data, which, when combined with original datasets, expanded data capacity enhanced functional connectivity. Moreover, GANs proved capable learning amplifying brain connectivity patterns present existing generating more distinctive features. A compact, two-layer 3D-CNN model was subsequently developed efficiently decode these TFPF Results: Taking into account session individual differences tests conducted on public GigaDB dataset SHU laboratory dataset. On dataset, our 3D-CNN-GAN models achieved two-class accuracies 76.49% 77.03%, respectively, demonstrating algorithm’s effectiveness improvement provided by augmentation. Furthermore, yielded 67.64% 71.63%, 58.06% 63.04%, respectively. Conclusions: algorithm enhances generalizability EEG-based (BCIs). Additionally, this research offers valuable insights potential applications BCIs.

Язык: Английский

Процитировано

3