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Spatial Effects of Electrode Configurations and Source Depth in EEG Source Localization: Implications for Concurrent
Summary
Electrode data loss near the brain source significantly impacts EEG source localization accuracy. Sparse and parametric inverse methods show better resilience to this degradation, crucial for brain stimulation studies.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Electroencephalography (EEG) source localization accuracy relies on high-quality electrode data.
- Electrode data loss is frequent in brain stimulation paradigms, yet its effect on localization is not well understood.
- Understanding this impact is vital for interpreting results from techniques like non-invasive brain stimulation coupled with EEG (NIBS-EEG).
Purpose of the Study:
- To evaluate how the depth of a brain source affects EEG source localization accuracy when electrodes are lost.
- To compare the performance of different inverse methods under various electrode degradation scenarios.
- To identify electrode processing strategies that maintain localization accuracy despite data loss.
Main Methods:
- Utilized the Localize-MI dataset with simultaneous stereoelectroencephalography (sEEG) and 256-channel EEG from epilepsy patients.
- Used single-pulse electrical stimulation for ground-truth source localization.
- Compared eight inverse methods (distributed, sparse, parametric) across five scenarios: baseline, proximal/distal electrode removal/interpolation.
Main Results:
- Proximal electrode data loss significantly increased localization error (PLE) and spatial dispersion (SD); distal loss had minimal impact.
- Dipole fitting (DF) and RAP-MUSIC showed lower PLE, while DF, RAP-MUSIC, and MxNE were most resilient to proximal degradation.
- Deeper sources showed increased PLE and SD, exacerbated by compromised nearby electrode data.
Conclusions:
- Electrode proximity to the source is critical for localization accuracy when data is degraded.
- Sparse and parametric inverse methods offer better robustness against localized electrode loss compared to distributed methods.
- Findings guide electrode management and inverse method selection in NIBS-EEG, especially when stimulation hardware obstructs sensors.
