用脑深部电刺激数据训练大模型，无需个体化调优即可解码帕金森症状。
Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
- 基于24天以上慢性脑电数据训练，上下文窗口达30分钟。
- 提出新损失函数，解决传统方法对低频信号的偏差问题。
- 跨患者测试中实现帕金森症状解码，无需个体微调。
神经状态解码可实现个性化的闭环神经调控治疗。近期预训练大模型的发展为无需个体化训练即可实现泛化状态估计提供了可能。本文基于超过24天的慢性深部脑刺激记录，构建了一个基础模型。考虑到症状在长时间尺度上的波动，模型采用30分钟的扩展上下文窗口。针对常见掩码自编码器损失函数因1/f功率律导致的频率偏差问题，我们设计了优化的预训练损失函数。在下游任务中，通过留一被试交叉验证，实现了无需个体化训练的帕金森病症状解码。
原文摘要 · Abstract (English)
Neural decoding of pathological and physiological states can enable patient-individualized closed-loop neuromodulation therapy. Recent advances in pre-trained large-scale foundation models offer the potential for generalized state estimation without patient-individual training. Here we present a foundation model trained on chronic longitudinal deep brain stimulation recordings spanning over 24 days. Adhering to long time-scale symptom fluctuations, we highlight the extended context window of 30 minutes. We present an optimized pre-training loss function for neural electrophysiological data that corrects for the frequency bias of common masked auto-encoder loss functions due to the 1-over-f power law. We show in a downstream task the decoding of Parkinson's disease symptoms with leave-one-subject-out cross-validation without patient-individual training.
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