EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

• Published in: Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics pp. 2958 - 2965
• Main Authors: Ingolfsson, Thorir Mar, Hersche, Michael, Wang, Xiaying, Kobayashi, Nobuaki, Cavigelli, Lukas, Benini, Luca
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.10.2020
• Subjects: Benchmark testing; Brain modeling; brain-machine interface; Computational complexity; Computational modeling; convolutional neural networks; deep learning; edge computing; Electroencephalography; Memory management; motor-imagery; Power demand

In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain-machine interfaces (MI-BMIs) based on electroencephalography (EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and compu...