The Capabilities of Chaos and Complexity
David Lynn Abel
Science Publishers eBooks September 7, 2011 DOI: 10.1201/b11408-13 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Argues that the brain is a nonlinear dynamic system whose EEG reflects chaotic activity, and that physiological EEG differs from pathological EEG, with a clear-headed brain showing greater chaotic degree, faster information processing, and more responses. Proposes comparing nonlinear dynamic EEG characteristics across consciousness states to examine relations between chaos and high-level intelligence activity. |
Abstract
EEG signal is a spontaneous bioelectricity activity that is produced by the central nervous system. It includes abundant information about the state and change of the neural system; therefore it is widely used in clinic and neural-electricity physiological research. In recent years, with the development of the nonlinear dynamics, more and more evidences indicate that the brain is a nonlinear dynamic system, and EEG signal can be regarded as its output [1,2]. In 1985, Babloyantz et al. fi rst put forward that II and IV stage EEG signals of human sleep cycle are chaotic [3]. Hereafter, a large number of study results were reported that the EEG was derived from chaotic systems [4-8]. Therefore, people try to analyze EEG signals by way of nonlinear dynamics to get new knowledge of the brain. Lindenberg, Lehnertz and Ferri et al. researched several kinds of physiological and pathologic conditions; and computed the relevant data under various conditions. They point out fi nally, the nonlinear characteristic of the physiological EEG signals greatly differs from that of the pathology; when clear-headed, the brain has higher chaotic degree, processes information more quickly and can make more responses [9-12]. Chaos is unordered, but in some situations, it has organizing structures and high order and is the source of system information [13]. Therefore, in this paper, we study the relation of chaos characteristic of EEG signals with high-level intelligence activity of human brain through comparative studies of the nonlinear dynamic characteristic of the dynamic physiological EEG information of brain under different consciousness conditions.