Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand
Meant to improve the overall quality of life for those with physical or motor impairments, this paper explores the use of EEG and its potential in controlling a prosthetic hand. EEG signal acquisition is centered on oscillatory features through the sensory motor rhythm which can be obtained through motor-imagery (MI). The EEGNet, a convolutional neural network, is used for feature extraction and signal classification of five motor-imagery classes of a hand. A reinforced model through a transfer learning approach deemed to have the best cross-validation accuracy. A real-time debugging module for the virtual hand was implemented using MuJoCo HAPTIX.
Limbaga, N.J., Mallari, K.L., Yeung, N.R., & Monje, J.C. (2022). Development of an EEG-based Brain-Controlled System for a Virtual Prosthetic Hand. Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, 1630-1633. https://doi.org/10.1109/BIBM55620.2022.9995382