A multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism for EEG-based motor imagery decoding

Computer Methods in Biomechanics & Biomedical Engineering, Год журнала: 2025, Номер unknown, С. 1 - 15

Опубликована: Янв. 6, 2025

Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism EEG-Based MI decoding. Specifically, first extracts representations raw signals, followed by independent branches to capture spatial, spectral, temporal-spatial, temporal-spectral features. Each branch includes domain-specific convolutional layer, variance layer. Finally, the derived each are concatenated weights classified through fully connected Experiments demonstrate outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% 2b, 74.58% OpenBMI, showcasing its potential robust applications.

Язык: Английский

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An improved multi-scale convolution and transformer network for EEG-based motor imagery decoding

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 4, 2025

Язык: Английский

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A composite improved attention convolutional network for motor imagery EEG classification

Frontiers in Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Фев. 5, 2025

A brain-computer interface (BCI) is an emerging technology that aims to establish a direct communication pathway between the human brain and external devices. Motor imagery electroencephalography (MI-EEG) signals are analyzed infer users' intentions during motor imagery. These hold potential for applications in rehabilitation training device control. However, classification accuracy of MI-EEG remains key challenge development BCI technology. This paper proposes composite improved attention convolutional network (CIACNet) classification. CIACNet utilizes dual-branch neural (CNN) extract rich temporal features, block module (CBAM) enhance feature extraction, (TCN) capture advanced multi-level concatenation more comprehensive representation. The model performs well on both IV-2a IV-2b datasets, achieving accuracies 85.15 90.05%, respectively, with kappa score 0.80 datasets. results indicate model's performance exceeds four other comparative models. Experimental demonstrate proposed has strong capabilities low time cost. Removing one or blocks decline overall model, indicating each within makes significant contribution its effectiveness. ability reduce costs improve (MI-BCI) systems, while also highlighting practical applicability.

Язык: Английский

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Self-supervised deep contrastive and auto-regressive domain adaptation for time-series based on channel recalibration

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 145, С. 110280 - 110280

Опубликована: Фев. 15, 2025

Язык: Английский

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Improving cross-session motor imagery decoding performance with data augmentation and domain adaptation

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 106, С. 107756 - 107756

Опубликована: Март 5, 2025

Язык: Английский

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Joint spatial feature adaption and confident pseudo-label selection for cross-subject motor imagery EEG signals classification

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127312 - 127312

Опубликована: Март 1, 2025

Язык: Английский

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Diffusion models-based motor imagery EEG sample augmentation via mixup strategy

Expert Systems with Applications, Год журнала: 2024, Номер 262, С. 125585 - 125585

Опубликована: Окт. 24, 2024

Язык: Английский

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Selective regularized spatial features representation learning for motor imagery EEG based on alternating cascaded model

Applied Soft Computing, Год журнала: 2024, Номер 165, С. 112087 - 112087

Опубликована: Авг. 9, 2024

Язык: Английский

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Efficient one-step multi-trial electroencephalograph spectral clustering via unsupervised covariance-based representations

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109502 - 109502

Опубликована: Окт. 21, 2024

Язык: Английский

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