Another approach to EEG analysis and interpretation is based on the non-linear dynamic systems theory dealing with complex, aperiodic systems capable of self-organization (Elbert et al., 1994; Pritchard and Duke, 1995). From this perspective, the EEG time series can be viewed as a complex structure reflecting the complexity of the dynamics of the underlying neural generators. It has been shown that dimensional complexity of the EEG can depend on the number of independently oscillating neuronal networks in the cortex and reflect a fine-tuned balance between chaotic and non-chaotic neuronal dynamics, a property that has important implications for normal adaptive brain functioning. Evidence from different neuroimaging modalities suggests that the human brain is a self-organized, large-scale complex system that operates in a critical state on the edge of order and chaos, which provides optimal conditions for flexible transitions between mental states as well as information processing, storage, and retrieval, whereas dysregulation of this fine-tuned balance between chaotic and non-chaotic brain dynamics can lead to breakdown of behavior observed in psychopathology (Birbaumer et al., 1995; Elbert et al., 1994; Kitzbichler et al.,
