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Improving Direction Decoding Accuracy During Online Motor Imagery Based Brain-computer Interface Using Error-related Potentials by Praveen K. Parashiva; A.P. Vinod is a Medicine article available to read on EtoBox.

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Decoding arm movement kinematics from motor imagery can provide more natural control of the Brain-Computer Interface (BCI) system. This work aims to decode two-directional (left versus right direction) motor imagery hand movement using Electroencephalogram (EEG)-based BCI. Direction decoding from EEG is challenging due to the low signal-to-noise ratio and poor spatial resolution. To improve the direction decoding accuracy during an online session, this work proposes an additional corrective step using Error-Related Potential (ErrP). Methods: In this work, a direction decoding model and an ErrP detection model are trained on calibration session data. During the online session, the trained direction decoding model uses segmented MI trials as input, and the decoded outcome is shown in real-time as feedback. The brain response to feedback shown is used to detect the presence of ErrP using a trained ErrP detection model. If the ErrP is detected, the outcome of the direction decoding model will be automatically corrected, which offers an improvement in the performance of the BCI system. Results: The online session is conducted on 14 healthy subjects' data. The average online direction de

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Author

Praveen K. Parashiva; A.P. Vinod

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Medicine (Physical Sciences)