A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM For MI-BCI Classification

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; p. 1
• Main Authors: Jia, Hai, Yu, Shiqi, Yin, Shunjie, Liu, Lanxin, Yi, Chanlin, Xue, Kaiqing, Li, Fali, Yao, Dezhong, Xu, Peng, Zhang, Tao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; attention mechanism; Brain; Brain modeling; Classification; Classification algorithms; Computer applications; Convolutional neural networks; Decoding; Deep learning; Domains; EEG; Electroencephalography; Feature extraction; Human-computer interface; Implants; LightGBM; Machine learning; Mental task performance; Modules; Motor imagery; Nervous system; Neural networks; Signal to noise ratio; spectral-temporal; Task analysis

Accurately decoding motor imagery (MI) brain-computer interface (BCI) tasks has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less subject information and low signal-to-noise ratio of MI electroencephalography (EEG) signals make it difficult to decode the...