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, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: Jan. 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.

Language: 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, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 4, 2025

Language: Английский

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A composite improved attention convolutional network for motor imagery EEG classification

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 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.

Language: 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, Journal Year: 2025, Volume and Issue: 145, P. 110280 - 110280

Published: Feb. 15, 2025

Language: Английский

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

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107756 - 107756

Published: March 5, 2025

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 127312 - 127312

Published: March 1, 2025

Language: Английский

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

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 262, P. 125585 - 125585

Published: Oct. 24, 2024

Language: Английский

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

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 165, P. 112087 - 112087

Published: Aug. 9, 2024

Language: Английский

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

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109502 - 109502

Published: Oct. 21, 2024

Language: Английский

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