Estimating the parameters of neural mass models including time delay and nonlinearity using a particle filter: a preliminary study toward model‐based EEG analysis

Abstract

Electroencephalogram (EEG) and local field potential (LFP) signals are measured for both experimental and clinical purposes which include sleep stage analyses, brain–computer interfaces, and disease diagnosis. EEG and LFP data analyses are typically based on models assuming that the measured data is generated from a biological system and estimate the model parameter values that most accurately reproduce the measured data. Thus, use of a biologically plausible model is important for a model‐based analysis. However, analyses using models that include time delay and nonlinearity have not been reported, even though they are biologically important for EEG generation mechanisms. In this study, we developed a parameter estimation method that uses a particle filter for models with time delay and nonlinearity, which was evaluated with simulations. Simulated EEG data were generated from neural mass models (NMMs). The NMM parameters were estimated from the generated data. Furthermore, parameters for modeling EEG features of patients with Alzheimer's disease were included in the NMM; the disease parameters were estimated from the simulated EEG data. We observed that NMM parameters, as well as the disease parameters, were accurately estimated from the simulated data. We conclude that the validity of our method for estimating parameters of NMMs including time delay and nonlinearity is confirmed for simulated EEG data, and these results show the possibility of using our method for model‐based analysis with real EEG data. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Robust Method with High Time Resolution for Estimating the Cortico-Thalamo-Cortical Loop Strength and the Delay when Using a Scalp Electroencephalography Applied to the Wake-Sleep Transition

“…The mean value and the standard deviation were 0.077 s and 0.00449 s, respectively. This result is consistent with those from with previous studies where t 0 was estimated with other methods such as curve fitting [22,23,27] or particle filter [24] and supports the validity of our method based on Eq. (8).…”

“…(4) and (1). In this study, M was set to 10 by using t 0 ≅80 ms, which was shown in previous studies [22,23,24,27], and T s =1/128 Hz. The RTF for the AR model is expressed as The same procedure that was used to derive Eq.…”

“…While some studies have demonstrated the validity and effectiveness of this strategy [22,23,24], further technical investigations into their practical and clinical applications are needed. One problem is the time resolution of the fitting, i.e., parameter estimation.…”

“…One problem is the time resolution of the fitting, i.e., parameter estimation. In most cases, a power spectrum is used for model fitting [22,23,24], but this process leads to the deterioration of time resolution because data collection over a long period of time (i.e., several minutes) is required in order to extract reliable power spectra from noisy (stochastic) EEG data. A second issue is the so-called "local minimum, " or nonuniqueness in the fitting (optimizing) procedure, arising from the substantial number of fitting parameters that exist, as well as the nonlinear contributions of parameters to the power spectrum.…”