A data assimilation method to track time-varying changes in the excitation-inhibition balance using scalp EEG

Recent neuroscience studies have suggested that control of the excitation and inhibition (E/I) balance is important to maintain normal brain function. However, an efficient method to evaluate the timevarying changes in E/I balance of the brain has yet to be established. To tackle this issue, we propose a new approach to estimate E/I balance changes, by applying the method of neural-mass model-based tracking of the brain state using the Ensemble Kalman Filter. In this method, the parameters regarding the synaptic E/I gains of the model are estimated from observed electroencephalography (EEG). Moreover, the index of E/I balance was defined by calculating the ratio between synaptic E/I gains based on estimated parameters. To validate this method, we applied it to numerical and human EEG data. As a result, we confirmed that our proposed method could estimate the E/I balance changes from observed human EEG.