Brain tissue classification from stereoelectroencephalographic recordings

• Published in: Journal of neuroscience methods Vol. 365; no. January; p. 109375
• Main Authors: Mulinari Pinheiro Machado, Mariana, Voda, Alina, Besançon, Gildas, Becq, Guillaume, Kahane, Philippe, David, Olivier
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2022; Elsevier
• Subjects: Bayes Theorem; Brain; Brain tissue classification; Electrodes, Implanted; Electroencephalography - methods; Empirical transfer function estimate; Epilepsies, Partial; Frequency identification; Humans; Life Sciences; Stereoelectroencephalography; Stereotaxic Techniques; Empirical transfer function estimate; Brain tissue classification; Stereoelectroencephalography; Frequency identification
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2021.109375

Stereoelectroencephalographic (SEEG) recordings can be performed before final resective surgery in some drug-resistant patients with focal epilepsies. For good SEEG signal interpretation, it is important to correctly identify the brain tissue in which each contact is inserted. Tissue classification is usually done with the coregistration of CT scan (with implanted SEEG electrodes) with preoperative MRI. Brain tissue classification is done here directly from SEEG signals obtained at rest by a linear discriminant analysis (LDA) classifier using measured SEEG signals. The classification operates on features extracted from Bode plots obtained via non-parametric frequency domain transfer functions of adjacent contacts pairs. Classification results have been compared with classification from T1 MRI following the labelling procedure described in Deman et al. (2018), together with minor corrections by visual inspection by specialists. With the data processed from 19 epileptic patients representing 1284 contact pairs, an accuracy of 72 ± 3% was obtained for homogeneous tissue separation. To our knowledge only one previous study conducted brain tissue classification using the power spectra of SEEG signals, and the distance between contacts on a shaft. The features proposed in our article performed better with the LDA classifier. However, the Bayesian classifier proposed in Greene et al. (2020) is more robust and could be used in a future study to enhance the classification performance. Our findings suggest that careful analysis of the transfer function between adjacent contacts measuring resting activity via frequency domain identification, could allow improved interpretation of SEEG data and or their co-registration with subject’s anatomy. •Brain tissue classification from SEEG signals, 72% accuracy achieved for 19 patients.•Features from transfer function are more discriminant than those in literature.•Method can be used as support to coregistration of CT scan with preoperative MRI.