Modeling EEG fractal dimension changes in wake and drowsy states in humans: a preliminary study

Bojić, T., Vuckovic, A. and Kalauzi, A. (2010) Modeling EEG fractal dimension changes in wake and drowsy states in humans: a preliminary study. Journal of Theoretical Biology, 262(2), pp. 214-222. (doi: 10.1016/j.jtbi.2009.10.001)

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Aim of this preliminary study was to examine and compare topographic distribution of Higuchi's fractal dimension (FD, measure of signal complexity) of EEG signals between states of relaxed wakefulness and drowsiness, as well as their FD differences. The experiments were performed on 10 healthy individuals using a fourteen-channel montage. An explanation is offered on the causes of the detected FD changes. FD values of 60 s records belonging to wake (Hori's stage 1) and drowsy (Hori's stages 2–4) states were calculated for each channel and each subject. In 136 out of 140 epochs an increase in FD was obtained. Relationship between signal FD and its relative alpha amplitude was mathematically modeled and we quantitatively demonstrated that the increase in FD was predominantly due to a reduction in alpha activity. The model was generalized to include other EEG oscillations. By averaging FD values for each channel across 10 subjects, four clusters (O2O1; T6P4T5P3; C3F3F4C4F8F7; T4T3) for the wake and two clusters (O2O1P3T6P4T5; C3C4F4F3F8T4T3F7) for the drowsy state were statistically verified. Topographic distribution of FD values in wakefulness showed a lateral symmetry and a partial fronto-occipital gradient. In drowsiness, a reduction in the number of clusters was detected, due to regrouping of channels T3, T4, O1 and O2. Topographic distribution of absolute FD differences revealed largest values at F7, O1 and F3. Reorganization of channel clusters showed that regionalized brain activity, specific for wakefulness, became more global by entering into drowsiness. Since the global increase in FD during wake-to-drowsy transition correlated with the decrease of alpha power, we inferred that increase of EEG complexity may not necessarily be an index of brain activation.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Vuckovic, Dr Aleksandra
Authors: Bojić, T., Vuckovic, A., and Kalauzi, A.
College/School:College of Science and Engineering > School of Engineering > Biomedical Engineering
Journal Name:Journal of Theoretical Biology
Published Online:12 October 2009

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