Can I read A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design on EtoBox?

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design by Komal Jindal; Rahul Upadhyay; Hari Shankar Singh is a Computer Science article available to read on EtoBox.

What is A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design about?

## Abstract Multi‐class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi‐channel EEG activity still remains a challenging task. In this study, to enhance the multi‐class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed. First, multi‐channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA‐SOGWO based EEG channel selection criterion. After that, the discriminable feature‐set is generated from time, frequency, and FAWT based time‐frequency domain of EEG dataset. Then, an informative feature‐set is constructed from extracted features through CCA‐RFE based feature selection criterion. Finally, training and validation of selected feature‐set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi‐class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the

Who reads A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design?

Author

Komal Jindal; Rahul Upadhyay; Hari Shankar Singh

Publisher

Wiley

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)