Singapore Institute of Technology

File(s) stored somewhere else

Please note: Linked content is NOT stored on Singapore Institute of Technology and we can't guarantee its availability, quality, security or accept any liability.

Improving direction decoding accuracy during online motor imagery based Brain-Computer Interface using error-related potentials

journal contribution
posted on 2023-09-26, 04:18 authored by Praveen K. Parashiva, Vinod Achutavarrier PrasadVinod Achutavarrier Prasad


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).


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.


The online session is conducted on 14 healthy subjects’ data. The average online direction decoding accuracy of the proposed direction decoding model without the ErrP-based corrective step is 54.9%. The average sensitivity and specificity of the proposed ErrP detection model during the online session are 65.3% and 67.5%, respectively. Further, the proposed scheme of cascaded direction decoding model and ErrP detection model achieves an average online direction decoding accuracy of 64.9%, which is an improvement of 10% compared to the scheme without the ErrP detection model.


This paper proposes the efficient use of the brain’s ErrP response to the visual neurofeedback presented to the user, in an online hand motor imagery direction decoding experiment paradigm. The significant improvement of the direction decoding accuracy achieved is a promising result that makes the existing BCI systems’ usability a step closer to practical real-world applications.


Journal/Conference/Book title

Biomedical Signal Processing and Control

Publication date


Usage metrics


    No categories selected


    Ref. manager