Applying Weightless Neural Networks to a P300-Based Brain-Computer Interface

P300-based Brain Computer Interfaces (BCI) are one of the most used types of BCIs in the literature that make use of the electroencephalogram (EEG) signal to convey commands to the computer. The efficiency of such systems depends drastically on the ability of correctly identifying the P300 wave in the EEG signal. Due to high inter-subject and inter-session variability, single-subject classifiers must be trained every session. In order to achieve fast setup times of the system, only a few trials are available each session for training the classifier. In this scenario, the capacity to learn from few examples is crucial for the performance of the BCI and, therefore, the use of weightless neural networks (WNN) is promising. Despite its possible added value, there are no studies, to our knowledge, applying WNNs to P300 classification. Here we compare the performance of a WNN against the state-of-the-art algorithms when applied to a P300-based BCI for joint-attention training in autism. Our results show that the WNN performs as good as its competitors, outperforming them several times. We also perform an analysis of the WNN hyperparameters, showing that smaller memories achieve better results most of the times. This study demonstrates that the adoption of this type of classifiers might help increase the prediction accuracy of P300-based BCI systems, and should be a valid option for future studies to consider.