Optimization of phase prediction for brain-state dependent stimulation: a grid-search approach

Abstract

Abstract Objective. Sources of heterogeneity in non-invasive brain stimulation literature can be numerous, with underlying brain states and protocol differences at the top of the list. Yet, incoherent results from brain-state-dependent stimulation experiments suggest that there are further factors adding to the variance. Hypothesizing that different signal processing pipelines might be partly responsible for heterogeneity; we investigated their effects on brain-state forecasting approaches. Approach. A grid-search was used to determine the fastest and most-accurate combination of preprocessing parameters and phase-forecasting algorithms. The grid-search was applied on a synthetic dataset and validated on electroencephalographic (EEG) data from a healthy (n = 18) and stroke (n = 31) cohort. Main results. Differences in processing pipelines led to different results; the grid-search chosen pipelines significantly increased the accuracy of published forecasting methods. The accuracy achieved in healthy was comparably high in stroke patients. Significance. This systematic offline analysis highlights the importance of the specific EEG processing and forecasting pipelines used for online state-dependent setups where precision in phase prediction is critical. Moreover, successful results in the stroke cohort pave the way to test state-dependent interventional treatment approaches.