MSCNN-ADDA: A Cross-Subject P300 EEG Decoding Algorithm Based on a Multi-Scale Convolutional Neural Network and Adversarial Discriminative Domain Adaptation

A brain-computer interface (BCI) enables direct communication between the brain and external devices. Despite progress, EEG decoding still faces challenges: 1) how to shorten or eliminate the calibration process in cross-subject BCI scenarios; 2) how to capture more characteristic features from different scales in EEG data; and 3) how to extract subject-independent EEG features more effectively. To address these, we propose a cross-subject EEG decoding algorithm based on a multiscale convolutional neural network (MSCNN) and domain adaptation for P300-based BCIs. The MSCNN was trained on a large-scale EEG dataset to extract subject-independent features, then fine-tuned via ADDA to align cross-subject data. In offline analysis, we achieved a cross-subject average accuracy exceeding 83%, indicating that we successfully established a domain adaptation-based cross-subject EEG decoding algorithm, which can eliminate the subject-specific calibration process for new subjects.