Development of a BCI master switch based on single-trial detection of contingent negative variation related potentials

To control the startup/shutdown of a conventional brain-computer interface (BCI) that is always running for daily use, we proposed and developed a new BCI system called a BCI master switch. We designed it with on/off switching functions by detecting the contingent negative variation (CNV) - related potentials. We chose CNV to improve the single-trial discrimination of user intentions to switch because CNV had a high signal-to-noise ratio and needed high concentration for its elicitation. We also applied a support vector machine (SVM) to improve the single-trial detection of CNV-related potentials. As the best parameters of SVM were estimated and applied, the offline evaluation's best performance achieved a CNV detection rate of 99.3% for the intention to switch and 2.1% for the intention not to switch. Remarkably, this performance was achieved from single-trial detection, imaginary response of user's intention without physical reaction, and the data from only one recording electrode. These results suggest that our proposed BCI system might work as a master switch by single-trial detection.