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Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy
[article]
2020
arXiv
pre-print
Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded
arXiv:2005.04881v1
fatcat:5s4arew6ufgypnbmwpzk7gvlpq