Freeing P300-Based Brain-Computer Interfaces from Daily Calibration by Extracting Daily Common ERPs

Abstract

When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs on a daily basis. We aim to address the daily calibration issue by examining across-day variation of the BCI performance and proposing a method to avoid daily calibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days in nineteen healthy subjects. We first examined how the BCI performance varied across days with or without daily calibration. On each day, P300-based BCIs were tested using calibration-based and calibration-free decoders (CB and CF), with a CB or a CF decoder being built on the training data on each day or those on the first day, respectively. Using the CF decoder resulted in lower BCI performance on subsequent days compared to the CB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the CF decoder and retested the BCI performance over days. Using the proposed method improved the CF decoder performance on subsequent days; the performance was closer to the level of the CB decoder, with improvement of accuracy by 2.28%, 1.93%, 1.75%, and 3.86 % on the subsequent four days, respectively, compared to the original CF decoder. The method proposed by our study may provide a novel approach to addressing the daily-calibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.