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

• URL: http://arxiv.org/abs/2508.10160v1
• Date: Wed, 13 Aug 2025 19:49:46 GMT
• Title: Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
• Authors: 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,
• Abstract summary: We present a foundation model trained on chronic longitudinal deep brain stimulation recordings spanning over 24 days.<n>Adhering to long time-scale symptom fluctuations, we highlight the extended context window of 30 minutes.<n>We show in a downstream task the decoding of Parkinson's disease symptoms with leave-one-subject-out cross-validation without patient-individual training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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