دورية أكاديمية
A diagonal masking self-attention-based multi-scale network for motor imagery classification.
| العنوان: | A diagonal masking self-attention-based multi-scale network for motor imagery classification. |
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| المؤلفون: | Yang K; Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China., Wang J; Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China., Yang L; Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China., Bian L; Frontier Institute of Chip and System, Fudan University, Shanghai 200433, People's Republic of China., Luo Z; Institute of Intelligent Manufacturing, Shunde Polytechnic, Foshan 528300, People's Republic of China., Yang C; Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China. |
| المصدر: | Journal of neural engineering [J Neural Eng] 2024 Jun 13; Vol. 21 (3). Date of Electronic Publication: 2024 Jun 13. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004- |
| مواضيع طبية MeSH: | Electroencephalography*/methods , Electroencephalography*/classification , Imagination*/physiology , Brain-Computer Interfaces*, Humans ; Neural Networks, Computer ; Movement/physiology |
| مستخلص: | Objective . Electroencephalography (EEG)-based motor imagery (MI) is a promising paradigm for brain-computer interface (BCI), but the non-stationarity and low signal-to-noise ratio of EEG signals make it a challenging task. Approach . To achieve high-precision MI classification, we propose a Diagonal Masking Self-Attention-based Multi-Scale Network (DMSA-MSNet) to fully develop, extract, and emphasize features from different scales. First, for local features, a multi-scale temporal-spatial block is proposed to extract features from different receptive fields. Second, an adaptive branch fusion block is specifically designed to bridge the semantic gap between these coded features from different scales. Finally, in order to analyze global information over long ranges, a diagonal masking self-attention block is introduced, which highlights the most valuable features in the data. Main results . The proposed DMSA-MSNet outperforms state-of-the-art models on the BCI Competition IV 2a and the BCI Competition IV 2b datasets. Significance . Our study achieves rich information extraction from EEG signals and provides an effective solution for MI classification. (© 2024 IOP Publishing Ltd.) |
| فهرسة مساهمة: | Keywords: convolutional neural network; fusion; motor imagery; multi-scale; self-attention |
| تواريخ الأحداث: | Date Created: 20240604 Date Completed: 20240613 Latest Revision: 20240613 |
| رمز التحديث: | 20240613 |
| DOI: | 10.1088/1741-2552/ad5405 |
| PMID: | 38834056 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1741-2552 |
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| DOI: | 10.1088/1741-2552/ad5405 |