Introduction

In the past two decades, a great deal of attention has been devoted to possible descriptions of electroencephalogram (EEG) activity in the context of chaos – that is, in terms of hypothetical underlying low-dimensional nonlinear dynamics, where trajectories that are initially close together diverge exponentially in time. This assumption is justified by neuron-synchronous firing and by the instability of EEG even to weak attentive stimuli. Moreover, Hebbian synaptic reinforcement implies that parallel synchronization should increase as a basic mechanism for perception and memory. The observed low dimension provides justification for the use of moderately populated artificial neural networks of low computational cost. Artificial neural networks are suitable to determine, or to reproduce, EEG rhythms on a macroscopic scale but are unable to simulate neurophysiological functions. In contrast, clusters of very few neurons, described by nonlinear differential systems that are highly expensive computationally, can realistically reproduce action potential generation and propagation mechanisms. The resulting electric signal traffic, crossing the brain, is sensitive to the electromagnetic (EM) perturbation, which can even alter functional correlates. This property legitimates transcranial magnetic stimulation (TMS) as a research tool to study aspects of human brain physiology with regard to motor function, vision, language, and brain disorders. TMS is also used in diagnostics and therapy.

Basics of Cortical Activity and EEG

The EEG essentially reflects the underlying electrical activity of the brain cortex.