The Problem of Segmental Description of Human Electroencephalogram

A critical review of the principal strategies of the EEG description as a piecewise stationary process is given. Achievements, problems, and prospects of parametric and nonparametric strategies of the EEG segment structure assessment are discussed on the basis of the literature and the author's data. Among them, attention is directed to the adequacy of the EEG segmentation based on the autoregression models or on the previous fragmentation of EEG realization in pieces of fixed duration, to the relative value of EEG segmentation on different time scales, and to the spatiotemporal integration of local segmental EEG descriptions. A possibility of the hierarchical segmental EEG descriptions on different time scales and frequency ranges is discussed. It is concluded that the set of modern strategies of the segmental EEG descriptions is, on the whole, sufficient for comprehensive exploration of the EEG process in terms of its piecewise stationary organization. A quarter of a century after Hans Berger had demonstrated the first recordings of electrical activity of the human brain, the rigorous qualitative estimation of the EEG signal raised the legitimate question of its statistical nature. Norbert Wiener proposed to consider the EEG as a stochastic signal by analogy with the output characteristics of any complex system [1]. It was thought that the principal regularities of the dynamics of the total EEG signal could be studied on the basis of its probability-statistical estimations irrespective of the real biophysical origin of cortical electrical processes [2]. Thus, a considerable body of work appeared concerning the stochastic properties of the EEG signal (for a review see [3]). The main conclusion is that the EEG may actually be approximated by the basic normal, ergodic, Gaussian, etc., stochastic criteria only at rather short realizations, usually not longer than 10-20 s. This is explained by the finding that the EEG turned out to be an extremely nonstationary process. The variability of power of the main spectral EEG components, e.g., for successive short-term (5-10 s) segments, reached 50-100% [4]. Obviously, the routine statistical characteristics (including the spectral-correlational ones) are applicable to the EEG signal only after its prior segmentation into relatively stationary intervals. This, in turn, necessitated development the detection techniques of the so-called quasistationary segments of the EEG signal. The first positive findings in this line not only pointed the way for more correct estimation of the EEG signal statistical properties but, more importantly, approached the principally novel understanding of the EEG organization as a piecewise stationary process [5]. The conceptual problems of the structural approach to the EEG analysis were reviewed by us earlier [3]. In the present paper, we discuss the achievements, problems, and prospects of EEG signal segmentation in itself. 1. EEG AS A NONSTATIONARY STOCHASTIC PROCESS The EEG nonstationarity is usually expressed as the short-term paroxysms (spikes, spike-waves, and K-complexes) or successively alternating longer shifts in the EEG parameters [6]. As a rule, phasic paroxysmal EEG phenomena are visually identified by researchers without question, while the detection of tonically stabilized EEG segments demands some theoretical justification.