Asynchronous detection of error-related potentials using a generic classifier

Error-related potentials (ErrPs) can be usedto improve BCIs’ performance but its use is often with-held by long calibration periods. We recorded EEG dataof 15 participants while controlling a robotic arm towardsa target. In 30 % of the trials, the protocol prompted anerror during the trial in order to trigger ErrPs in the partic-ipants. For each participant, we trained an ErrP classifierusing the data of the remaining 14 participants. Each ofthese classifiers was tested asynchronously on the data ofthe selected participant. The threshold that maximizedthe product of the average true positive rate (TPR) andthe average true negative rate (TNR) wasτ=0.7. For thisthreshold, the average TPR was 53.6 % and the averageTNR was 82.0 %. These results hint at the feasibility oftransferring ErrPs between participants as a reliable strat-egy to reduce or even remove the calibration period whentraining ErrP classifiers to be used in an asynchronous manner