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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
Published on: June 15, 2018
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Long-term stability of resting state EEG-based linear and nonlinear measures
Toomas Põld1, Laura Päeske2, Hiie Hinrikus2
1Tallinn University of Technology, School of Information Technologies, Department of Health Technologies, Centre for Biomedical Engineering, Tallinn, Estonia; Qvalitas Medical Centre, Tallinn, Estonia.
Summary
This study assessed electroencephalography (EEG) measure stability over three years in healthy adults. Nonlinear measures like Higuchi
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Electroencephalography (EEG) is crucial for brain activity monitoring.
- Assessing the long-term stability of EEG measures is vital for clinical applications.
- Linear and nonlinear EEG analysis techniques offer different insights into brain function.
Purpose of the Study:
- To evaluate the longitudinal stability of various linear and nonlinear EEG measures in healthy adults over a three-year period.
- To compare the stability of different EEG measures, including relative band powers, interhemispheric (IHAS) and spectral (SASI) asymmetries, Higuchi's fractal dimension (HFD), and detrended fluctuation analysis (DFA).
Main Methods:
- Resting-state, eyes-closed EEG data were collected from 17 healthy adults across two sessions, three years apart.
- Linear EEG measures (relative powers, IHAS, SASI) and nonlinear measures (HFD, DFA) were calculated.
- Statistical analysis was performed to assess the stability (test-retest reliability) of each measure.
Main Results:
- Nonlinear measures (HFD, DFA) and single-channel, single-band relative power measures demonstrated the highest stability.
- Measures utilizing single channels and multiple frequency bands (SASI) showed moderate stability.
- Measures requiring signals from multiple channels (IHAS) exhibited the lowest stability over the three-year interval.
Conclusions:
- Nonlinear EEG measures and simpler linear measures exhibit robust long-term stability.
- The findings support the potential utility of stable EEG-based biomarkers in longitudinal clinical assessments.
- EEG measure selection should consider the trade-off between complexity and stability for reliable clinical use.
