  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 with novel wavelet features, we
achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement
vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs
Precision, demonstrating the effectiveness of these features in EEG-based grasp
differentiation. In contrast to previous studies, a critical part of our study
was permutation feature importance analysis, which highlighted key features for
grasp classification. It revealed that the most crucial brain activities during
grasping occur in the motor cortex, within the alpha and beta frequency bands.
These insights demonstrate the potential of wavelet features in real-time
neuroprosthetic technology and BCI applications.
