Improving direction decoding accuracy during online motor imagery based brain-computer interface using error-related potentials

•Decoding the motor movement direction from the Motor Imagery (MI) task can provide more natural control of the Brain-Computer Interface (BCI).•The efficient use of the response to the feedback to correct the mistakes of the MI decoding model during an online session can improve the classification accuracy of the MI-BCI system.•This work proposes to train two models – a MI direction decoding model and an Error-Related Potential detection model using the calibration session data and test them in a closed-loop online MI session.•The online session conducted on 14 subjects achieves an improvement of 10% direction decoding accuracy using the proposed method.•The significant improvement in the classification accuracy achieved by efficiently using the neurofeedback response is a promising result that can take the existing MIBCI system a step closer to practical real-world applications. 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 o