Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

arXiv:2402.09447v1 Announce Type: cross
Abstract: This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques …

abstract analysis applications arxiv bci brain brain-computer interface computer cs.ai cs.lg decode development eeg eess.sp natural noninvasive patients power precision q-bio.nc research type types wavelet