Spatio-spectral feature classification combining 3D-convolutional neural networks with long short-term memory for motor movement/imagery

• Published in: Engineering applications of artificial intelligence Vol. 120; p. 105862
• Main Authors: Huang, Wenqie, Chang, Wenwen, Yan, Guanghui, Zhang, Yuchan, Yuan, Yueting
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2023
• Subjects: Brain network analysis; Brain-computer interface (BCI); Convolutional neural networks (CNNs); Electroencephalogram (EEG); Long short-term memory (LSTM); Motor imagery (MI); Motor imagery (MI); Brain-computer interface (BCI); Brain network analysis; Convolutional neural networks (CNNs); Electroencephalogram (EEG); Long short-term memory (LSTM)

In this paper, we propose a novel EEG classification approach based on the Spatio-spectral feature, aiming to design a motor movement/imagery classification model that extracts multi-domain features with promising performance. Firstly, the difference between motor imagery (MI) tasks and real executi...