Methods of analyses of biological time series are presented and compared to the traditional techiiiques based on the Fourier transform. Paranietric methods are used for computation of the autoregressive estimator, for the model order selection and for the spectrum estirnation. A nonlinear analysis deals with the state-space trajectory reconstruction and with the fractal and embedding dirnension estirnation. Experimental resiilts compare the abilities of traditional, pararnetric and nonlinear methods to distinguish different cognitive States of the human operator by an analysis of an EEG curve.
Impaired wakefulness of machine operators presents a danger not only for themselves, but often for the public at large as well. While on duty, such persons are expected to be continuously, i.e. without interruption, on the alert. For that purpose, we designed and carried out an experimental model of continuous vigilance monitoring using electroencephalography (EEG) and reaction time measured as the latency of the volunteers’ reaction to a sound stimulus. In this article, we focus on two different approaches of EEG signal analysis. Spectral analysis, which is based on linear stochastic approach, is the hrst type. On the other hand, there is nonlinear analysis formally called the chaos theory. For both methods, we will show typical markers which represent the state of the vigilance. Both methods will be compared and the outputs will be discussed.
This paper presents advanced methodology for the analysis of the electroencephalographic activity (EEG) of the brain aimed to monitor the cognitive states of an operator. The methodology of EEG analysis is based on two main approaches: linear methods based on Fourier transform, Linear Stochastic Models, Multi-covariance analysis, and nonlinear methods based on estimation of state space attractor, state space dimension, D2 dimension and the Largest Lyapunov Exponent (LLE). The correct application of these methods is supported by the study of stability, dynamics and space distribution of EEG signal. The uncertainty of adopting a new methodology, such as presented chaos theory, for EEG signal analysis is minimized by the adequate setup of experiments and by evaluation of results against well adopted power spectral estimates calculated by Fourier transform. For better understanding of the underlying processes behind EEG, the basic mental states such as relaxation, single and complex number count, and Raven test are analyzed and compared with the vigilance states. The averaged behavior of the computed markers of the EEG signal is studied with respect to a reaction time scale by the evaluation of a set of experiments. Because of this complex approach, the presented methodology is able to track the ongoing changes in EEG activity during the process of falling asleep. The automatic detection of vigilance changes is a consequent step to this work. Usability of such device in various fields of everyday life is of the high importance.
The article addresses the overwhelming problematics of attention decrease of human operators. It presents two sets of experiments used for vigilance detection and possible microsleep prediction. In the hrst experiment, the analysis of the electroencephalographic activity (EEG) of a human operátor (proband) is correlated with the reaction time (RT) to the sound stimulus. For the second set of experiments, the cooperation of a car simulator realized in virtual reality (VR) environment and measurement of EEG is presented. The paper introduces two main methods of the analysis of EEG: frequency analysis and nonlinear analysis based on computation of the state-space trajectory. For the frequency analysis, the delta, theta, alpha and beta bands are computed and compared with nonlinear measures Largest Lyapunov Exponent (LLE) and Correlation dimension (CD). Finally, both experiments are compared and its outcomes are discussed.