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