A Closed-loop Adaptive Brain-computer Interface Framework: Improving the Classifier with the Use of Error-related Potentials

Brain-computer interfaces (BCIs) using Electroencephalography (EEG) have drawn attention to providing alternative control pathways for users with motor disabilities or even the general public in real-world environments due to their robustness, relatively low cost, and high portability. However, EEG still suffers from large variability between subjects or between sessions of an individual subject. To obtain optimal performance, a BCI usually requires a user to go through a calibration process to fine-tune the model. This calibration process is usually long and could hinder the practicality of a BCI. In this study, we propose a closed-loop framework that monitors the user EEG responses to the action of a BCI. If an Error-related Potential (ErrP) is detected in the response, it is indicated that the BCI is making a wrong prediction. By using the information from this ErrP detector, we can include online testing trials into the training pool and further fine-tune the model over the time the BCI is used. Results suggest that the proposed framework can reach better results with a few additional trials when compared to the model pre-trained from some existing data. Also, the performance of the proposed model can gradually converge to a fully calibrated model, which suggests that the conventional calibration process could be replaced by online training.