How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection

Abstract

Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvement real-time event-related potential (ERP) detection. Variability and low signal-to-noise ratio (SNR) impair detection methods. We hypothesized that if in a P300-based BCI we find the electrodes with the maximum relative voltage area (the “maximum relative” term refers to the area within each trial, but not between trials) where a P300 can be located, we will improve the performance of a classifier and reduce the number of trials necessary to achieve 100% success. We propose a method that calculates successively the maximum relative voltage areas in the P300 region of the EEG signal for each stimulus. In this way, differences between a target and a non-target stimulus are maximized. This method was tested with a linear classifier (LDA), known for its good performance and low computational cost. We observed that a single electrode with maximum relative voltage area in a P300 region can give more information than the traditional 4 electrode measurement. The preliminary results show that by detecting appropriate characteristics in the EEG signal, we can reduce the error by trial as well as the number of electrodes. The detection of the maximum relative voltage area in the EEG electrodes is a characteristic that can contribute to increase the SNR and decrease the prediction error with the smallest number of trials in the P300-based BCI systems. This type of methods that seek specific characteristics in the signals can also contribute to the management of the variability present in the BCI systems. This method can be used both for an online and offline analysis.