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

Author(s):  
Takayuki Onojima ◽  
Keiichi Kitajo
Author(s):  
Alberto Ferrari ◽  
Pieter Ginis ◽  
Michael Hardegger ◽  
Filippo Casamassima ◽  
Laura Rocchi ◽  
...  

2021 ◽  
Author(s):  
Takayuki Onojima ◽  
Keiichi Kitajo

We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for closed-loop sensory stimulation in electroencephalography (EEG) experiments. 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. We demonstrate that the performance of our method shows higher accuracy in predicting the EEG phase than the conventional autoregressive model-based method. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated autoregressive 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.


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