Accurate detection of P300 event-related potentials from electroencephalography (EEG) remains challenging for small numbers of trials due to low signal-to-noise ratios and substantial inter-subject variability. This study presents a systematic comparison of data aggregation strategies for improving P300 classification, evaluated on a 10-subject dataset using two convolutional neural network architectures (EEGNet and BaseCNN) and a support vector machine (SVM). We compared: (1)~subject-specific and pooled general models for single trials; (2)~epoch averaging with 5 and 10 stimuli repetitions; (3)~multi-channel models where subjects corresponded to different input channels; (4)~cross-subject averaging; (5)~mixed (uncontrolled) averaging; (6)~a combined approach with $K$ trials per subject across all participants; and (7)~time-shifted channels from extended single-trial epochs. Decoding performance was quantified using the Information Transfer Rate (ITR), computed for binary classification accuracy. We found that single-trial ITR was unpractical (0.15--0.64~bits/trial), whereas controlled aggregation improved the performance. The combined cross-subject approach with $K=3$ trials per participant (30 channels) achieves the highest ITR with multi-channel EEGNet: 0.95~bits/aggregated decision in the no-aperture recordings and 0.97~bits/aggregated decision on Aperture data, approaching the theoretical binary-classification limit for the aggregated decision. Controlled cross-subject averaging consistently outperformed random trial mixing, and multi-channel architectures outperformed simple averaging when inter-subject structure was preserved. These findings contribute to improving P300 decoding and implementing multi-subject brain-computer interfaces (BCIs).
Sidorov, L., Makarova, A., Maysuradze, A., Lebedev, M.
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