Neurophysiological Characterization of the BigP3BCI Dataset and Preliminary Machine Learning Evaluation for P300-Based Brain–Computer Interfaces

The P300 component is a well-established event-related potential (ERP) widely employed in brain–computer interface (BCI) systems, particularly in visual oddball paradigms such as the P300 speller. In this study, we analyze the large-scale BigP3BCI dataset to characterize the neurophysiological properties of P300 responses and to perform a preliminary evaluation of machine learning-based classification. EEG recordings were segmented into epochs aligned to stimulus onset and averaged separately for Target and non-target conditions to compute ERPs. The analyses revealed differences between Target and non-target responses, with a predominantly centro-parietal distribution broadly consistent with the classical P3b component. Although the grand-average ERP exhibited an earlier positive deflection, additional analyses performed separately across study/paradigm groups demonstrated substantial variability in ERP morphology and peak latency, highlighting the heterogeneous composition of the dataset. A preliminary machine learning analysis was subsequently performed using Logistic Regression and shrinkage Linear Discriminant Analysis (LDA). Classification performance improved progressively with epoch averaging, reaching an area under the receiver operating characteristic curve (AUC) of approximately 0.82 after averaging ten epochs. Performance was initially evaluated using five-fold stratified cross-validation at the classification-example level. To assess generalization under stricter leakage-controlled conditions, additional group-disjoint analyses were performed using leave-one-participant-out, leave-one-study-out, and leave-one-paradigm-out validation. In these analyses, train/test groups were defined before epoch averaging, which was performed separately within each partition and restricted to epochs from the same participant, session, and class. Classification performance increased with epoch averaging under both the preliminary example-level and group-disjoint validation schemes. With ten-epoch averaging, mean ROC-AUC reached approximately 0.81 under participant-disjoint validation, 0.78 under study-disjoint validation, and 0.77 under paradigm-disjoint validation. Overall, the study provides a large-scale neurophysiological characterization of BigP3BCI together with interpretable linear-classification baselines and an initial assessment of cross-participant, cross-study, and cross-paradigm generalization.