Improving bit rate in an auditory BCI: Exploiting error-related potentials

Abstract

The error-related potential (ErrP) can inform the correction of brain-computer interface (BCI) mistakes, but it has thus far been incorporated only into visual BCIs, with mixed success. Given that ErrPs are thought to have higher impact when BCI accuracy is relatively low, we sought to identify the aurally evoked ErrP and investigate its auto-corrective value in auditory BCIs, which typically yield lower accuracies than visual BCIs. We implemented an auditory P300 BCI with four selectable items. Each of nine typically developed participants attempted to spell letter sequences on two separate days. Erroneous feedback was detected by (i) making use of the ErrP, (ii) assessing BCI selection confidence, and (iii) combining these two pieces of information into a hybrid detector. ErrPs were detected with an average cross-validation area under the curve of 0.946. Simulated automatic correction by reverting to the second-ranked letter improved participant-wise information transfer rate by 2.3 bits/minute when errors were detected by the hybrid method. The results suggest ErrP-based error correction can be used to make a substantial improvement in the performance of auditory BCIs.