Abstract
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.
Highlights
Closed-loop neuroscience is gaining more attention with ongoing technological and innovative advances that enable complex feedback loops to be executed with millisecond resolution on hardware
The results suggest that we succeeded in outputting triggers targeting specific phases of alpha oscillations in a real-time implementation under resting conditions, specific phases of alpha oscillations in a real-time implementation under resting condidoing this with both YW-based and least mean square (LMS)-based AR models
All the results indicate that we succeeded in outputting the triggers targeting specific phases of alpha oscillations in a real-time implementation, doing this with both YW-based and LMS-based AR models except for one participant in one condition
Summary
Closed-loop neuroscience is gaining more attention with ongoing technological and innovative advances that enable complex feedback loops to be executed with millisecond resolution on hardware. With regard to brain mechanics, much has been learned about stimulation in an open-loop manner using a pre-defined stimulus, such as the determination of input–output characteristics and how these are possibly modified. This open-loop approach has been quite productive in the field of non-invasive brain stimulation (NIBS), facilitating major developments in pharmacological understanding, as well as in the understanding of the functional basis of cortical dynamics [1,2]. By creating a causal relationship between the stimulus generator and the measured output, one can possibly “close the loop” [3] in laboratory settings In reality, this can be achieved when a presented stimulus depends on the simultaneously measured instantaneous brain state. The neuronal output of the brain affects the input to the brain, closing the loop [3]
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