零样本框架提升脑电跨人跨任务解码，无需校准即可预测行为。
Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
- 用渐进解冻策略优化Transformer，实现零样本跨被试解码
- 新方法在未见被试上将误差降至0.9799，优于基线的0.9991
- 适合计算精神病学与无校准脑机接口研究者参考
开发通用脑电（EEG）解码模型对构建稳健的脑机接口和精神健康领域的客观神经生物标志物至关重要。传统方法受限于高个体差异性和非平稳神经信号，导致跨被试与跨任务泛化能力差。本文基于大规模健康大脑网络数据集，提出一种零样本跨被试解码框架，对比了卷积神经网络基线、混合LSTM以及基于Transformer的通用模型。为使Transformer适用于回归任务并避免灾难性遗忘，提出新颖的渐进解冻策略。基线模型nRMSE为0.9991，而微调后的Transformer在未见被试上达到0.9799。该工作推动了可扩展、免校准的脑电解码，适用于计算精神病学与行为预测。
原文摘要 · Abstract (English)
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.
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