Decoding pre-movement neural activity from thalamic LFPs for adaptive neurostimulation in tremor patients.

Objective: To advance adaptive deep brain stimulation for tremor disorders, we investigated the feasibility of using machine learning to decode pre-movement oscillatory changes in thalamic local field potentials (LFPs) and scalp electroencephalography (EEG) signals. Our aim was to predict upcoming upper-limb movements based on these neural signals. Approach: We recorded and analysed from 11 patients undergoing deep brain stimulation surgery for the treatment of tremor, employing machine learning models-including logistic regression, gradient-boosted decision trees, and convolutional neural networks-to distinguish rest periods from pre-movement periods. Main results: We demonstrate that early neural correlates can predict movement onset, achieving above-chance decoding performance starting approximately 430 ms before movement initiation using thalamic LFP and 840 ms using EEG signals. Individualised, patient-specific decoders outperformed cross-patient models, reflecting inter-patient variability in neural modulatory patterns. Additionally, multiple frequency bands contributed independently to decoding performance, highlighting the importance of incorporating a spectrum of frequencies rather than relying solely on activity in any single canonical band. Significance: These findings underscore the value of personalised, multi-band machine learning-based approaches for capturing the neural correlates preceding movement. They support the development of adaptive neurostimulation therapies through tailored models that account for patient-specific patterns in neural activity.