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Updated: Jun 1, 2026
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis
Ana Catarino1, Owen Churches, Simon Baron-Cohen
1Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Campo Grande 1749-016 Lisbon, Portugal. ac655@medschl.cam.ac.uk
Objective:
Intrinsic complexity subserves adaptability in biological systems. One recently developed measure of intrinsic complexity of biological systems is multiscale entropy (MSE). Autism spectrum conditions (ASC) have been described in terms of reduced adaptability at a behavioural level and by patterns of atypical connectivity at a neural level. Based on these observations we aimed to test the hypothesis that adults with ASC would show atypical intrinsic complexity of brain activity as indexed by MSE analysis of electroencephalographic (EEG) activity.
Methods:
We used MSE to assess the complexity of EEG data recorded from 15 participants with ASC and 15 typical controls, during a face and chair matching task.
Results:
Results demonstrate a reduction of EEG signal complexity in the ASC group, compared to typical controls, over temporo-parietal and occipital regions. No significant differences in EEG power spectra were observed between groups, indicating that changes in complexity values are not a reflection of changes in EEG power spectra.
Conclusions:
The results are consistent with a model of atypical neural integrative capacity in people with ASC.
Significance:
Results suggest that EEG complexity, as indexed by MSE measures, may also be a marker for disturbances in task-specific processing of information in people with autism.
