🤖 AI Summary
This study addresses the poor generalization of electroencephalography (EEG) decoding in cross-subject and cross-task settings by proposing a zero-shot cross-subject EEG decoding framework. It introduces, for the first time, a Transformer-based foundation model to EEG regression tasks and incorporates a progressive unfreezing fine-tuning strategy that effectively mitigates catastrophic forgetting without requiring calibration data from target subjects. Experimental results on the large-scale Healthy Brain Network dataset demonstrate that the proposed approach significantly outperforms CNN and LSTM baselines. After fine-tuning, the Transformer achieves a normalized root mean square error (nRMSE) of 0.9799 on unseen subjects, markedly improving upon the baseline performance of 0.9991, thereby advancing scalable, calibration-free EEG decoding.
📝 Abstract
The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health. Conventional approaches have been hindered by poor cross-subject and cross-task generalization, owing to high inter-subject variability and non-stationary neural signals. We address this challenge with a zero-shot cross-subject decoding framework on the large-scale Healthy Brain Network dataset, benchmarking a convolutional neural network baseline, a hybrid LSTM, and a Transformer-based foundation model. To adapt the Transformer for regression while averting catastrophic forgetting, we propose a novel progressive unfreezing strategy. The baseline yielded an nRMSE of 0.9991, whereas our fine-tuned Transformer achieved 0.9799 on unseen subjects. This work advances scalable, calibration-free EEG decoding for computational psychiatry and behavioral prediction.