Nonlinear EEG analysis and its potential role in epileptology

Deterministic chaos offers a striking explanation for apparently irregular behavior of the brain that is evidenced in the EEG. Recent developments in the physical-mathematical framework of the theory of nonlinear dynamics (co lloquially often termed chaos theory) provide new concepts and powerful alg orithms to analyze such time series. Because of its high versatility, nonli near time series analysis has already gone beyond the physical sciences and , at present, is being successfully applied in a variety of disciplines, in cluding cardiology, neurology, psychiatry, and epileptology. However, it is well known that different influencing factors limit the use of nonlinear m easures to characterize EEG dynamics in a strict sense. Nevertheless, when interpreted with care, relative estimates of, e.g., the correlation dimensi on or the Lyapunov exponents, can reliably characterize different states of normal and pathologic brain function. In epileptology, extraction of nonli near measures from the intracranially recorded EEG promises to be important for clinical practice. In addition to an immense reduction of information content of long-lasting EEG recordings, previous studies have shown that th ese measures enable (a) localization of the primary epileptogenic area in d ifferent cerebral regions during the interictal state, (b) investigations o f antiepileptic drug effects, (c) analyses of spatio-temporal interactions between the epileptogenic zone and other brain areas, and (d) detection of features predictive of imminent seizure activity. Nonlinear time series ana lysis provides new and supplementary information about the epileptogenic pr ocess and thus contributes to an improvement in presurgical evaluation.