A data assimilation method to track excitation-inhibition balance change using scalp EEG

• Published in: Communications engineering Vol. 2; no. 1; pp. 92 - 12
• Main Authors: Yokoyama, Hiroshi, Kitajo, Keiichi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.12.2023; Springer Nature B.V; Nature Portfolio
• Subjects: 631/378; 631/378/1697; Brain; Data assimilation; Electroencephalography; Engineering; Excitation; Kalman filters; Learning; Mathematical models; Methods; Normal distribution; Parameter estimation; Transcranial magnetic stimulation; Variables
• ISSN: 2731-3395; 2731-3395
• DOI: 10.1038/s44172-023-00143-7

Recent neuroscience studies have suggested that controlling the excitation and inhibition (E/I) balance is essential for maintaining normal brain function. However, while control of time-varying E/I balance is considered essential for perceptual and motor learning, an efficient method for estimating E/I balance changes has yet to be established. To tackle this issue, we propose a method to estimate E/I balance changes by applying neural-mass model-based tracking of the brain state using the Ensemble Kalman Filter. In this method, the parameters of synaptic E/I gains in the model are estimated from observed electroencephalography signals. Moreover, the index of E/I balance was defined by calculating the ratio between synaptic E/I gains based on estimated parameters. The method was validated by showing that it could estimate E/I balance changes from human electroencephalography data at the sub-second scale, indicating that it has the potential to quantify how time-varying changes in E/I balance influence changes in perceptual and motor learning. Furthermore, this method could be used to develop an E/I balance-based neurofeedback training method for clinical use. Accurate control of excitation and inhibition balance change is important for brain perceptual and motor learning. Yokoyama and Kitajo report a data assimilation-based method to track the excitation and inhibition balance change via analysing electroencephalography signals. The method was validated using data for human beings in sleep.