Optimal referencing for stereo-electroencephalographic (SEEG) recordings.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Stereo-electroencephalography (SEEG) is a crucial intracranial recording technique for presurgical epilepsy evaluation.
• SEEG enables comprehensive neural signal sampling from both cortical and subcortical brain regions.
• Optimal signal referencing is critical for accurate SEEG data interpretation, yet has not been systematically studied.

Purpose of the Study:

• To comprehensively evaluate and compare five different re-referencing methods for SEEG data.
• To determine the optimal referencing strategy for enhancing SEEG signal quality and analysis.

Main Methods:

• SEEG data were recorded from 15 human subjects during a motor task, initially using a monopolar reference.
• Five re-referencing methods were applied: Common Average Reference (CAR), Gray-White matter Reference (GWR), Electrode Shaft Reference (ESR), bipolar reference, and Laplacian reference.
• Signal quality was assessed using three distinct metrics to compare the effectiveness of each re-referencing approach.

Main Results:

• The Laplacian re-reference method demonstrated superior performance across multiple signal quality metrics.
• Specific benefits of the Laplacian reference include improved analysis of local population-level neural activity.
• The Laplacian reference is particularly effective for studying low-frequency oscillatory activity in SEEG recordings.

Conclusions:

• The Laplacian re-reference is recommended as the optimal method for analyzing SEEG data, especially for local activity and low-frequency oscillations.
• This finding provides crucial guidance for researchers and clinicians aiming to maximize the information obtained from SEEG recordings.
• Further validation across diverse SEEG applications may be warranted.