Pre-trained Transformer-models using chronic invasive electrophysiology for symptom decoding without patient-individual training
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Merk, Timon, Salehi, Saeed, Koehler, Richard M., Cui, Qiming, Olaru, Maria, Hahn, Amelia, Provenza, Nicole R., Little, Simon, Abbasi-Asl, Reza, Starr, Phil A., Neumann, Wolf-Julian
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6979342247
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