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.
|Title of host publication||2019 Signal Processing Symposium (SPSympo)|
|Number of pages||4|
|Publication status||Published - 2019|
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Signal Processing