Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training

Timon Merk; Saeed Salehi; Richard M. Koehler; Qiming Cui; Maria Olaru; Amelia Hahn; Nicole R. Provenza; Simon Little; Reza Abbasi-Asl; Phil A. Starr; Wolf-Julian Neumann

arXiv · 2508.10160

Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training

Timon Merk·Saeed Salehi·Richard M. Koehler·Qiming Cui·Maria Olaru·Amelia Hahn·Nicole R. Provenza·Simon Little·Reza Abbasi-Asl·Phil A. Starr·Wolf-Julian Neumann

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Abstract

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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Timon Merk, Saeed Salehi, Richard M. Koehler, Qiming Cui, Maria Olaru, Amelia Hahn, Nicole R. Provenza, Simon Little, Reza Abbasi-Asl, Phil A. Starr, Wolf-Julian Neumann. 2025-08-13. Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training. https://arxiv.org/abs/2508.10160

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