Machine Learning and Stereoelectroencephalographic Feature Extraction for Brain Tissue Classification

Abstract

Abstract Tissue classification of white or gray matter is a necessary information in the study of brain connectivity. Currently this classification is made by the coregistration of the implanted electrodes in the Magnetic Resonance Imaging (MRI) of the patient. This process is complex and therefore is not always carried out, and is limited by the image resolution and by the accuracy of the coregistration. This paper studies the performance of machine learning (ML) algorithms used with features extracted from Stereo-Electroencephalogram (SEEG) signals recorded from three epileptic patients, for electrode contact classification, to serve as a decision support for specialists and researchers. The features are based on epileptic detection, and are extracted from both time and frequency domain. Accuracy, Area Under Curve and F1-Score are evaluated for each ML algorithm, and feature importance is assessed by feature permutation. Satisfactory results were achieved, with a maximum of 79% accuracy in group separation for patient specific classification, and 74% in inter-patient classification, indicating high potential in ML techniques for brain tissue classification.