Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain
Xingyuan Wang, Juan Meng, Gui-Lin Tan, Lixian Zou
Nonlinear biomedical physics April 27, 2010 DOI: 10.1186/1753-4631-4-2 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Population | Human subjects performing five consciousness activities |
| Measures | central tendency measure (CTM), approximate entropy, correlation dimension, Lyapunov exponent |
| Key findings | The authors conclude that chaos characteristics may lie in human consciousness activities, that central tendency measure can divide EEG attractors consistently with phase graphs, and that correlation dimension and Lyapunov exponent indicate fractional-dimensional attractors. Approximate entropy varied between subjects, with higher entropy linked to better innovation, and a neural-network classifier distinguished the five activities, with arithmetic showing better differentiation than abstract tasks. |
Abstract
Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.