Kalman smoother based time-varying spectrum estimation of EEG during single agent propofol anesthesia

A time-varying parametric spectrum estimation method for analyzing EEG dynamics is presented. EEG signals are first modeled as a time-varying auto-regressive stochastic process and the model parameters are estimated recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated parameters. The proposed method was applied to measurements collected during low dose propofol anesthesia. The method was able to detect changes of event related (de)synchronization type elicited by verbal command.

On correlation between single-trial ERP and GSR responses: a principal component regression approach

In this study we investigate the correlation between single-trial evoked brain responses and galvanic skin responses (GSR). The correlation between the two signals is examined by using a modified principal component regression based approach. A potential application of the study is to utilize the GSR measurements in a form of a prior information in the estimation of the brain potentials when only small number of trials is available.