A Transfer Learning Approach for Adaptive Classification in P300 Paradigms

Introduction: The P300 is one of the most widely used brain responses in BCIs today, popularized by none other than the P300 speller itself. However, most systems still require significant subject-specific training to achieve accurate, reliable classification of brain signals. We present an approach to classification that allows for classification with zero subject-specific data and also improves as data is collected. It does this through the use of data from other subjects in order to intelligently regularize the subject-specific solution with a prior over the weight vector. This approach has already been validated on spectral data [1] and so by validating on P300 data as well we show that it is a classification technique that is agnostic to how features are computed from the EEG time series so long as there are multiple subjects or sessions involved. We further introduce a novel method for estimating parameters that drastically reduces the time necessary to implement transfer learning.