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Physics-Informed Attention Temporal Convolutional Network for EEG-Based Motor Imagery Classification
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DOI：10.1109/TII.2022.3197419.png)
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摘要
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The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assist disabled people, control devices or environments, and even augment human capabilities. However, the limited performance of brain signal decoding is restricting the broad growth of the BCI industry. In this article, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification. The ATCNet model utilizes multiple techniques to boost the performance of MI classification with a relatively small number of parameters. ATCNet employs scientific machine learning to design a domain-specific deep learning model with interpretable and explainable features, multihead self-attention to highlight the most valuable features in MI-EEG data, temporal convolutional network to extract high-level temporal features, and convolutional-based sliding window to augment the MI-EEG data efficiently. The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV-2a dataset with an accuracy of 85.38% and 70.97% for the subject-dependent and subject-independent modes, respectively.
Keyword:
Attention
classification
convolution neural network (CNN)
deep learning
electroencephalography (EEG)
motor imagery
scientific machine learning
temporal convolution networks (TCN)
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9.9
论文数:
8.6K
被引数:
6.0W
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引用论文
Attention-Inception and Long- Short-Term Memory-Based Electroencephalography Classification for Motor Imagery Tasks in Rehabilitation用于康复运动图像任务的注意力开始和基于长短期记忆的脑电图分类
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分享收藏Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning.png)
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