Extraction of "deep" components from scalp EEG.

Area of Science:

• Neuroscience
• Biophysics
• Medical Imaging

Background:

• Electroencephalography (EEG) is crucial for diagnosing epilepsy, particularly absence seizures characterized by 3 per second generalized spike-wave discharges.
• Differentiating between superficial and deep brain electrical activity in EEG signals remains a challenge for precise localization of seizure origins.
• Understanding the spatial distribution of EEG activity is key to improving diagnostic accuracy and therapeutic strategies for petit mal seizures.

Purpose of the Study:

• To develop and validate a mathematical method for separating superficial and deep EEG components in patients with absence seizures.
• To investigate the contribution of deep brain structures to the EEG during ictal periods of generalized spike-wave discharges.
• To characterize the spatial patterns of deep EEG activity associated with petit mal seizures.

Main Methods:

• A novel mathematical approach was devised to split EEG signals into superficial and deep components based on electrical field characteristics.
• Source derivation was employed to suppress broad potential fields, isolating deep generator contributions by subtracting source density from electrode potential values.
• Eigenfunction analysis was applied to the derived deep EEG data, followed by topographic mapping to visualize electrode contributions.

Main Results:

• The methodology successfully separated EEG activity into distinct superficial and deep components.
• Analysis of ictal EEGs from three patients consistently revealed that the "deep" data primarily yielded two mutually perpendicular components.
• Topographic mapping demonstrated the spatial distribution of these deep EEG components during absence seizures.

Conclusions:

• The developed mathematical method effectively delineates deep brain electrical activity during absence seizures.
• The findings suggest that deep generators contribute distinct, spatially organized patterns to the EEG during generalized spike-wave discharges.
• This approach offers a promising tool for better understanding the neurophysiological basis of petit mal seizures and potentially improving their diagnosis.