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

Aqsa Shakeel^1,2, Takayuki Onojima¹, Toshihisa Tanaka^1,2

¹CBS-TOYOTA Collaboration Center, RIKEN Center for Brain Science, Wako 351-0198, Japan.

Journal of Personalized Medicine

|January 14, 2021

Summary

This study demonstrates that an adaptive least mean square (LMS)-based autoregressive (AR) model can effectively predict electroencephalography (EEG) signals in real-time. This enables precise, phase-locked triggering for closed-loop brain-computer interfaces.

Keywords:

Instantaneous phase Yule–Walker (YW) method alpha oscillation autoregressive (AR) model brain state-dependent stimulation closed-loop electroencephalography (EEG)least mean square (LMS) method

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Assessing instantaneous brain states via electroencephalography (EEG) in real-time closed-loop systems is challenging due to the need for future signal prediction.
Conventional Yule-Walker (YW)-based autoregressive (AR) models are used for real-time prediction, but adaptive methods for brain state-dependent closed-loop systems remain unexplored.

Purpose of the Study:

To investigate the real-time implementability of an adaptive least mean square (LMS)-based autoregressive (AR) model for time-series forward prediction in closed-loop systems.
To develop and evaluate a method for generating EEG state-dependent triggers synchronized with specific phases of alpha oscillations.

Main Methods:

An adaptive least mean square (LMS)-based autoregressive (AR) model was employed for real-time time-series forward prediction of EEG signals.
EEG state-dependent triggers were synchronized with the peaks and troughs of alpha oscillations during both resting (eyes-open) and visual task conditions.
The model's performance was evaluated across all participants for both resting and visual task states.

Main Results:

The proposed adaptive LMS-based AR model successfully generated triggers synchronized with specific phases of EEG alpha oscillations for all participants.
Statistical analysis confirmed the method's efficacy in both resting and visual task conditions.
The LMS-based AR model demonstrated successful real-time implementation in a closed-loop system targeting specific alpha oscillation phases.

Conclusions:

The adaptive LMS-based AR model is a viable and effective alternative for real-time EEG signal prediction in closed-loop systems.
This approach offers an adaptive solution with a low computational load, outperforming conventional and machine learning methods for phase-specific triggering.
The findings pave the way for more sophisticated real-time brain-computer interfaces and neurological monitoring applications.