A Novel Performance Metric for Multiclass Subject Invariant Brain Computer Interfaces with Imbalanced Classes

Abstract [Finding suitable common feature sets for use in multiclass subject independent brain-computer interface (BCI) classifiers is problematic due to characteristically large inter-subject variation of electroencephalographic signatures. We propose a wrapper search method using a one versus the rest discrete output classifier. Obtaining and evaluating the quality of feature sets requires the development of appropriate classifier metrics. A one versus the rest classifier must be evaluated by a scalar performance metric that provides feedback for the feature search algorithm. However, the one versus the rest discrete classifier is prone to settling into degenerate states for difficult discrimination problems. The chance of occurrence of degeneracy increases with the number of classes, number of subjects and imbalance between the number of samples in the majority and minority classes. This paper proposes a scalar Quality (Q)-factor to compensate for classifier degeneracy and to improve the convergence of the wrapper search. The Q-factor, calculated from the ratio of sensitivity to specificity of the confusion matrix, is applied as a penalty to the accuracy (1-error rate). This method is successfully applied to a multiclass subject independent BCI using 10 untrained subjects performing 4 motor tasks in conjunction with the Sequential Floating Forward Selection feature search algorithm and Support Vector Machine classifiers.]