Verlag des Forschungszentrums Jülich
JUEL-3608
Peters, Björn Olaf
Mulit-channel EEG time series analysis
63 S., 1998
Since half a century, the question is discussed, to which extent mental activity is
represented in the human electroencephalogram (EEG). In today's EEG
recording devices, signals from numerous electrodes can be analysed with
practically arbitrary sampling rates. The mathematical structure of EEG signals
is, however, not fully understood yet. To develop a method that automatically
deals with such complex, multi-channel information was scope of the thesis.
In order to not deal with the mathematically ill-posed problem of source
localization, a classification task was chosen to study EEG. Movements of left
and right index finger, and right foot were recognized in the EEG of three
subjects.
EEG data from a memorized delay task was analysed. The EEG data sets
were recorded on 56 electrodes positioned around motor-cortical areas on the
skull, then scanned manually, and only sets free from disturbing artifacts in the
signals were considered for later analysis. After passing the signals through
some spatial and temporal filters, parametrized estimates of the spectral density
were computed. On these compactified data sets, artificial neural nets were
trained and combined in a "committee of experts". Correct recognition was
achieved in 85% to 98% of EEG data slices recorded just around the movements
took place.
In a first chapter, event-related EEG potentials are characterized, the idea of
classification as a time series procedure and a possible application -a brain
computer interface -are presented. Then, the experimental setup and the
filtering and classification algorithms are motivated and introduced. In a third
chapter, the classifier is analysed for its general properties. In the last two
chapters, the classification results obtained on EEG's recorded from three
different subjects are presented and discussed.
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