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Besides conventional power spectral analysis and computed estimates of the dimensional complexity (D2) of the EEG and ERP time series, we use analysis approaches that give equal weight to the data's temporal and spatial characteristics. In these approaches, preference is given to data-driven procedures where top-down con- straints are kept to a minimum. Three approaches are: (1) Parsing of the series of brain electric momentary maps into epochs of quasi-stable topography, the so-called microstates. Since different momentary potential maps must have been generated by the activity of different neural assemblies, it is reasonable to assume that they represent different brain functional states, i.e., different steps or modes of information processing. Examining the series of momentary field maps showed that the changes of map landscape are discontinuous, stepwise. Hence, there is no continual development of brain state; rather, there are distinct, brief states concatenated by rapid state transitions in the sub-second time range. This lead to the concept of brain electric microstates, the manifestation of hypothetical 'atoms of thought', i.e., to the possibility to identify a repertoire of building blocks of mentation. Overviews of concept and results are in Lehmann 1994, 1995. A general solution to the parsing problem is presented in Pascual- Marqui et al. (1995), using a boot-strapping approach. (2) Source modelling of the potential fields in the time domain (a novel approach which requires minimal assumptions is presented in Pascual-Marqui et al. 1994), and source modelling in the frequency domain (the latter method was developed by the KEY Institute in 1990). (3) Computation of the global dimensional complexity of the brain electric signals. In addition to the conventional dimensional complexity single time series (after state space reconstruction following Takens' procedure), we apply computations of the 'global dimensional complexity' of the trajectory of the brain state (as assessed by the momentary maps) in state space, using either D2-calculations (where the embedding dimension is the number of simultaneously recorded time series or the independent measure OMEGA (e.g., see 'chewing gum...' in section 'Chemical Effects').
Key words: power spectral analysis - microstates - state transitions - 'atoms of thought' - source modelling of the potential field - dimensional complexity - D2


  • Pascual-Marqui, R.D., Michel, C.M. and Lehmann, D. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Transactions of Biomedical Engeneering 42: 658-665 (1995).
  • Lehmann, D. Die hirnelektrischen Bausteine des Denkens. UniZürich - Magazin der Universität Zürich, Nr.4, 32-34 (1994).
  • Lehmann, D. Brain electric microstates, and cognitive and perceptual states. In: P. Kruse and M. Stadler (eds): Multistability in Cognition. Springer, Berlin, 1995. pp. 407-420.
  • Pascual-Marqui, R.D., Michel C.M. and Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology 18: 49-65 (1994).