Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children

• Published in: 2019 Signal Processing Symposium (SPSympo) pp. 126 - 129
• Main Authors: Kotiuchyi, Ivan, Seleznov, Ivan, Faes, Luca, Pernice, Riccardo, Kharytonov, Volodymyr, Popov, Anton
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2019
• Subjects: brain connectivity; Brain modeling; Coherence; common spatial patterns; EEG; Electroencephalography; Epilepsy; epileptic seizures; Estimation; ICA; Transfer functions; VAR model
• DOI: 10.1109/SPS.2019.8882099

In this research, the study of functional connectivity between sources of electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) were obtained for EEG segments describing 95% of Riemannian distance between pairs of classes, followed by estimation of multivariate autoregressive (MVAR) models' coefficients. The MVAR models were further used to extract coherence as a functional connectivity measures. Our results show that the coherence between CSP sources differs between baseline and pre-ictal segments: it has the larger values in low and high frequency ranges during pre-ictal segment. This might correspond to increased coupling of slow and fast oscillations just before the seizure onset which poses a defining attribute of epileptiform activity. Our results indicate that a reorganization of EEG source activations occurs before the onset of focal seizures, which is promising for seizure prediction algorithms.