Takayuki Onojima

This paper introduces a deep neural network framework that directly predicts the complex-valued analytic signal of EEG from raw data, outperforming traditional methods and showing robustness to missing electrodes.

This paper introduces a Kalman filter-based method for real-time estimation of EEG oscillatory phase to enable closed-loop sensory stimulation, showing higher accuracy than autoregressive models.