Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model

• Published in: Journal of personalized medicine Vol. 11; no. 1; p. 38
• Main Authors: Shakeel, Aqsa, Onojima, Takayuki, Tanaka, Toshihisa, Kitajo, Keiichi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.01.2021; MDPI
• Subjects: Algorithms; Closed loop systems; Computational neuroscience; EEG; Electroencephalography; Learning algorithms; Oscillations; Precision medicine; Signal processing; Transcranial magnetic stimulation; Visual stimuli; Finland; Instantaneous phase; electroencephalography (EEG); closed-loop; least mean square (LMS) method; alpha oscillation; autoregressive (AR) model; brain state-dependent stimulation; Yule–Walker (YW) method
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm11010038

It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule–Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.