A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

ABSTRACT: Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact remova

SUBMITTER: Khan RA 

PROVIDER: S-EPMC10490872 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature