The classification of EEG-based winking signals: a transfer learning and random forest pipeline.

ABSTRACT: Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate

SUBMITTER: Mahendra Kumar JL 

PROVIDER: S-EPMC8019310 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature