A state-informed stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

Abstract

Objective.We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for state-informed sensory stimulation in electroencephalography (EEG) experiments.Approach.The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation.Main results.Our method showed higher accuracy in predicting the EEG phase than the conventional autoregressive (AR) model-based method.Significance.A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated AR model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

Keywords: EEG; Kalman filter; autoregressive model; instantaneous phase estimation; real-time system; state-informed stimulation.