Publications
Methodology
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
datadriven procedures where topdown con straints are kept to a
minimum. Three approaches are: (1) Parsing of the series of brain
electric momentary maps into epochs of quasistable topography, the
socalled 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 subsecond 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 bootstrapping approach. (2) Source modelling of the
potential fields in the time domain (a novel approach which requires
minimal assumptions is presented in PascualMarqui 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 D2calculations (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
References
 PascualMarqui,
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: 658665 (1995).
 Lehmann,
D. Die hirnelektrischen Bausteine des Denkens. UniZürich  Magazin der
Universität Zürich, Nr.4, 3234 (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. 407420.
 PascualMarqui,
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: 4965 (1994).
