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research-article

Quantification of event related brain patterns for the motor imagery tasks using inter-trial variance technique

Authors:

Fatemeh Shahlaei,

M. Ramasubba ReddyAuthors Info & Claims

Volume 126, Issue PB

https://doi.org/10.1016/j.engappai.2023.106863

Published: 01 February 2024 Publication History

Abstract

Quantification of event-related (de) synchronization (ERD/ERS) patterns is a challenging task in the field of motor imagery (MI)-based brain–computer interface (BCI). Accurately determining the optimal time and frequency band for localizing the ERD/ERS brain patterns is crucial in designing a robust MI-based BCI. To address above issue, this study proposes an inter-trial variance (IV) technique that focuses on localizing the ERD/ERS brain patterns and classifying the left and right hand imaginations of the subjects. The effectiveness of the proposed technique is validated using the BCI Competition-IV, Dataset-2b, which includes raw EEG signals from nine healthy subjects performing both left and right hand MI movements. In this technique, the sensorimotor frequency band (8–30 Hz) and its ERD/ERS brain patterns related to the left and right hand MI movements are derived from the signals. The obtained brain patterns are fed into various ML models, and the efficiency of the models is assessed using the classification accuracy (%CA) and Cohen’s kappa coefficient (K). The outcomes show that the proposed technique enhances the performance of the BCI system, classifying both left and right hand imaginations of the subjects with higher %CA (86.11%) and K (0.72), and outperforming the state-of-the-art techniques. This concludes that the ERD and ERS brain patterns extracted from the proposed technique are significant features in designing the MI-based BCI system.

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Cited By

Reddy CReddy M(2024)Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interfaceDigital Signal Processing10.1016/j.dsp.2024.104720155:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.dsp.2024.104720

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Quantification of event related brain patterns for the motor imagery tasks using inter-trial variance technique

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Information & Contributors

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Published In

cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 126, Issue PB

Nov 2023

1566 pages

ISSN:0952-1976

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

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Cited By

Reddy CReddy M(2024)Nonlinear difference subspace method of motor imagery EEG classification in brain-computer interfaceDigital Signal Processing10.1016/j.dsp.2024.104720155:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.dsp.2024.104720

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