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

Abstract

<i>Objective.</i>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.<i>Approach.</i>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 (<i>n</i>= 18) and stroke (<i>n</i>= 31) cohort.<i>Main results.</i>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.<i>Significance.</i>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.