Processing Electrophysiological Signals for the Monitoring of Alertness

This research project is concerned with the use of mathematical techniques for processing EEG signals associated with varying states of alertness. It requires the development and implementation of advanced signal modeling and data processing techniques; especially designed for the representation and prediction of bioelectric signals useful as estimators of states of alertness. In particular, our goal is to develop and implement efficient techniques for processing and modeling of EEG signals to extract the characteristics of signals associated with varying states of alertness. New represents±ionq for REG signals which will enhance the features in estimating the states of alertness are sought after. Fast algorithms for implementing real-time computations of alertness estimates have also been developed. In this report, we present a new realization of the phase-distortionless digital filter which approaches real-time filtering and a new transform for EEG signals. This transform not only provides new information for the alertness estimates but also can be performed in real time. We are also developing a statistical test for stationarity in EEG signals. This test provides a method for determining the duration of the EEG signals necessary in estimating the short-time power or energy spectra for nonstationary analysis of EEG signals. It also helps in extracting the dynamic properties of the signal process.