Towards stimulation-free automatic electrocorticographic speech mapping in neurosurgery patients

Abstract

Objective The precise mapping of speech-related functions is crucial for successful neurosurgical interventions in epilepsy and brain tumor cases. Traditional methods like Electrocortical Stimulation Mapping (ESM) are effective but carry a significant risk of inducing seizures. Methods To address this, we have prepared a comprehensive ESM+ECM dataset from 14 patients with chronically implanted stereo-EEG electrodes. Then we explored several compact machine learning (ML) approaches to convert the Electrocorticographic Mapping (ECM) signals to the ground truth derived from the risky ESM procedure. Both procedures involved the standard picture naming task. As features, we used gamma-band power within successive temporal windows in the data averaged with respect to picture and voice onsets. We focused on a range of classifiers, including XGBoost, Linear Support Vector Classification, Regularized …