Single Trial Detection of EEG Error Potentials: A Tool for Increasing BCI Transmission Rates

This paper presents a pattern recognition approach that allows for a robust single trial detection of this error potential from multichannel EEG signals and designs classifiers that are capable of bounding false positives (FP), which would classify correct responses as errors.

Abstract

It is a well-known finding in human psychophysics that a subject's recognition of having committed a response error is accompagnied by specific EEG variations that can easily be observed in averaged event-related potentials (ERP). Here, we present a pattern recognition approach that allows for a robust single trial detection of this error potential from multichannel EEG signals. By designing classifiers that are capable of bounding false positives (FP), which would classify correct responses as errors, we achieve performance characteristics that make this method appealing for response-verification or even response-correction in EEG-based communication, i.e., brain-computer interfacing (BCI). This method provides a substantial improvement over the choice of a simple amplitude threshold criterion, as it had been utilized earlier for single trial detection of error potentials.