On the use of interaction error potentials for adaptive brain computer interfaces

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods.

► We propose a new adaptive classification method for Brain Computer Interfaces (BCI). ► This method uses Interaction Error Potentials (IErrPs) as a reinforcement signal. ► We confirm that the IErrPs are possible to detect at the single trial level. ► We study the method wrt the false positive/negative rates on IErrP classification. ► We show that this method can significantly improve static classification methods.