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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
Published on: October 24, 2012
ESTIMATION OF DIRECTIONAL BRAIN ANISOTROPY FROM EEG SIGNALS USING THE MELLIN TRANSFORM AND IMPLICATIONS FOR SOURCE
Catherine Stamoulis1, Bernard S Chang
1Harvard Medical School, Children's Hospital Boston, Departments of Neurology and Radiology, Clinical Research Program, 300 Longwood Ave., Boston MA 02115, USA.
International Conference on Digital Signal Processing Proceedings : DSP. International Conference on Digital Signal Processing
|September 18, 2012
Summary
This study introduces a new method using the Mellin transform to analyze electroencephalogram (EEG) signals during seizures. Correcting for signal scaling improves the accuracy of estimating seizure propagation paths for better source localization.
Area of Science:
- Neuroscience
- Signal Processing
- Biomedical Engineering
Background:
- Epileptic seizures generate complex electroencephalogram (EEG) signals with contributions from multiple propagation paths.
- Brain's directional anisotropy influences seizure-related EEG signal components, complicating analysis.
- Accurate source localization of epileptic activity is crucial for effective treatment.
Purpose of the Study:
- To develop a novel method for estimating frequency-specific EEG scale modulations using the Mellin transform.
- To investigate the impact of brain's directional anisotropy on EEG signals during epileptic seizures.
- To assess how correcting for non-linear EEG scaling affects time-delay estimation for source localization.
Main Methods:
- Utilized the Mellin transform to analyze EEG data.
- Estimated patient-specific, direction-specific, frequency-locked scale shifts.
- Assessed the effect of scale modulations on time-delay estimation.
Main Results:
- Identified frequency-locked scale shifts at frequencies ≥50 Hz during the ictal interval.
- Demonstrated that correcting for non-linear EEG scaling improves time-delay estimation.
- Observed larger time-delays when EEGs were corrected by a scale factor.
Conclusions:
- The proposed method effectively estimates frequency-specific EEG scale modulations influenced by brain anisotropy.
- Corrections for non-linear EEG scaling can enhance time-delay estimation accuracy.
- This approach holds promise for improving source localization in epilepsy, especially for rapidly spreading seizures.
