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

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....

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Bibliographic Details
Published in:International journal of imaging systems and technology 2022-07, Vol.32 (4), p.1318-1337
Main Authors: Jindal, Komal, Upadhyay, Rahul, Singh, Hari Shankar
Format: Article
Language:English
Subjects:
brain computer interface
channel selection
Channels
Classification
Competition
Criteria
Datasets
electroencephalogram
Feature extraction
Information retrieval
machine learning
Methodology
motor imagery
Optimization
Systems design
wavelet transform
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Summary: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 proposed methodology as an efficient method for future BCI system design.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22700