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Adaptive classification to reduce non-stationarity in visual evoked potential brain-computer interfaces

Kapgate Deepak, Kalbande Dhananjay, Shrawankar Urmila

Bio-Algorithms and Med-Systems

2019

Vol. 15, no. 2

art. no. 20190020

Non-stationarity of electroencephalogram (EEG) signals greatly affect classifier performance in brain-computer interface (BCI). To overcome this problem we propose an adaptive classifier model known as extended multiclass pooled mean linear discriminant analysis (EMPMLDA). Here, we update the average class pair co-variance matrix along with pooled mean values. Evaluation of classifiers are done on visual evoked cortical potential data-sets. We demonstrate that EMPMLDA can significantly outperform other static classifiers such as MLDA and adaptive classifiers (MPMLDA). Furthermore an optimal update coefficient can be achieved using different datasets.