Detection Of Error Correlates In The Motor Cortex In A Long Term Clinical Trial Of Ecog Based Brain Computer Interface

Error correlates are thought to be promising for BCIs as a way to perform error correction or prevention, or to label data in order to perform online adaptation of BCIs' control models. Current state-of-the-art BCIs are motor-imagery-based invasive BCIs and thus have no access to neural data apart from sensory-motor cortices. We investigated at the single trial level the presence and detectability of error correlates in the primary motor cortex during observation or motor imagery (MI) control of a BCI with two discrete classes by a tetraplegic user. We show that error correlates can be detected using a broad range of classifiers, namely Support Vector Machine (SVM), logistic regression, N-way Partial Least Squares (NPLS), Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) with respective mean AUC of the ROC curve of 0.645, 0.662, 0.642, 0.680 and 0.630 in the observation condition, and 0.623, 0.605, 0.603, 0.626 and 0.580 in the MI-control condition. We also suggest that these error correlates are stable in time. These findings suggest that error correlates could be used in clinical trials using invasive motor-imagery-based BCIs for error correction or prevention.