Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

• Published in: Micromachines (Basel) Vol. 13; no. 6; p. 927
• Main Authors: Chang, Zhanyuan, Zhang, Congcong, Li, Chuanjiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022; MDPI
• Subjects: Adaptive algorithms; Algorithms; Alignment; Artificial neural networks; Brain research; brain-computer interface; Classification; data alignment; Deep learning; Discriminant analysis; Electroencephalography; Human-computer interface; Image classification; Machine learning; Methods; motor imagery; Neural networks; Recognition; Signal processing; Support vector machines; transfer learning; China
• ISSN: 2072-666X; 2072-666X
• DOI: 10.3390/mi13060927

For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.