Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects

Abstract

The intersubject variability present in electroencephalographic (EEG) signals can affect the performance of the brain–computer interface (BCI) systems. Despite the significant progress in the field, the variability in neural data remains one of the most critical challenges in constructing accurate predictive models of human intention. As a result, the majority of the previous studies have focused either on devising subject-specific signal processing and machine learning algorithms, used some data from a target user to update and calibrate a pretrained classifier, or have used data collected from a relatively large number of training subjects to construct generic classifiers for new subjects. In this work, we investigate the feasibility of using a relatively small number of training subjects to achieve subject-independent classification of event-related potentials (ERPs) in P300-based BCIs. To this end, we employ convolutional neural networks (CNNs) and propose a leave-one-subject-out cross-validation (LOSO-CV) for model selection; that is to say, for tuning CNN hyperparameters including number of layers, filters, kernel size, and epoch. The utility of the proposed model selection is warranted because LOSO-CV simulates the effect of subject-independent classification within the training data. The entire process of training (including model selection) is validated by applying another LOSO-CV external to the training process. Our empirical results obtained on four publicly available datasets confirm the capability of LOSO-CV model selection with CNN to capture intrinsic ERP features from a small group of subjects to classify observations collected from unseen subjects.