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Open AccessFeature PaperArticle

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

1
CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako 351-0198, Japan
2
Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
3
Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8585, Japan
4
Department of Physiological Sciences, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Okazaki 444-8585, Japan
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2021, 11(1), 38; https://doi.org/10.3390/jpm11010038
Received: 13 November 2020 / Revised: 5 January 2021 / Accepted: 6 January 2021 / Published: 11 January 2021
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. View Full-Text
Keywords: electroencephalography (EEG); brain state-dependent stimulation; closed-loop; autoregressive (AR) model; Yule–Walker (YW) method; least mean square (LMS) method; alpha oscillation; Instantaneous phase electroencephalography (EEG); brain state-dependent stimulation; closed-loop; autoregressive (AR) model; Yule–Walker (YW) method; least mean square (LMS) method; alpha oscillation; Instantaneous phase
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MDPI and ACS Style

Shakeel, A.; Onojima, T.; Tanaka, T.; Kitajo, K. Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model. J. Pers. Med. 2021, 11, 38. https://doi.org/10.3390/jpm11010038

AMA Style

Shakeel A, Onojima T, Tanaka T, Kitajo K. Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model. Journal of Personalized Medicine. 2021; 11(1):38. https://doi.org/10.3390/jpm11010038

Chicago/Turabian Style

Shakeel, Aqsa; Onojima, Takayuki; Tanaka, Toshihisa; Kitajo, Keiichi. 2021. "Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model" J. Pers. Med. 11, no. 1: 38. https://doi.org/10.3390/jpm11010038

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