دورية أكاديمية
ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain–Computer Interface
العنوان: | ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain–Computer Interface |
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المؤلفون: | Wei Tao, Ze Wang, Chi Man Wong, Ziyu Jia, Chang Li, Xun Chen, C. L. Philip Chen, Feng Wan |
المصدر: | IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 154-165 (2024) |
بيانات النشر: | IEEE, 2024. |
سنة النشر: | 2024 |
المجموعة: | LCC:Medical technology LCC:Therapeutics. Pharmacology |
مصطلحات موضوعية: | Convolutional neural networks (CNNs), motor imagery (MI), brain–computer interface (BCI), self-attention mechanism, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950 |
الوصف: | Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain–computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG $\boldsymbol{\mu }$ and $\boldsymbol{\beta }$ rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1558-0210 |
Relation: | https://ieeexplore.ieee.org/document/10356088/; https://doaj.org/toc/1558-0210 |
DOI: | 10.1109/TNSRE.2023.3342331 |
URL الوصول: | https://doaj.org/article/fa456324347b4b5896eb0233c185af0f |
رقم الأكسشن: | edsdoj.fa456324347b4b5896eb0233c185af0f |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 15580210 |
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DOI: | 10.1109/TNSRE.2023.3342331 |