MOTION-STIMULATED EEG RECOGNITION BY YOLO DEEP NETWORK

• Published in: Journal of mechanics in medicine and biology Vol. 24; no. 2
• Main Author: ZHANG, XU
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.03.2024; World Scientific Publishing Co. Pte., Ltd
• Subjects: Attention; Brain; Electroencephalography; Fourier transforms; Modules; motor stimulation; EEG recognition; YOLO network; lightweight
• ISSN: 0219-5194; 1793-6810
• DOI: 10.1142/S0219519424400244

In order to elucidate the relationship between sportsman motion awareness and brain waves from a physiological perspective, research was conducted on the recognition of motion-stimulated brain waves. A motion-stimulated electroencephalogram (EEG) recognition method based on the You Only Look Once (YOLO) deep network is proposed to address the high complexity, high interference, strong randomness, and strong time-varying characteristics of EEG signals. Under the YOLO deep network framework, replacing the Efficient Layer Aggregation Networks-A (ELAN-A) module with the Convolution-Next (CvNX) module achieves the goal of lightweight processing of the YOLO network. Furthermore, the Simple Attention Mechanism (SimAM) attention mechanism is introduced, and a special loss function is set. During the experiment, the short-time Fourier transform was used to convert the EEG signals of the test subjects into time-frequency EEG images with rich features, which can be used as inputs for the YOLO network. The experimental results show that our method achieves recognition accuracy of over 90% and the highest of over 95% in the recognition of five types of motion-induced EEG waves. Compared to CNN and traditional YOLO methods, our method has 3–5% improved with recognition accuracy.