Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
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Electrical Engineering and Systems Science > Signal Processing
Title:Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task 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.
| Subjects: | Signal Processing (eess.SP); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.23706 [eess.SP] |
| (or arXiv:2606.23706v1 [eess.SP] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23706
arXiv-issued DOI via DataCite
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Submission history
From: Baimam Boukar Jean Jacques [view email][v1] Fri, 12 Jun 2026 02:04:56 UTC (333 KB)
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