Document Type : Full Research Paper


1 M.Sc. Student, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

2 Assistant Professor, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

3 Assistant Professor, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran / Associate Professor, Telecommunications and Information Processing Department, Ghent University, Ghent, Belium


Different perceptual, cognitive and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brains response in the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective and functional, s.t. the first one refers to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional and effective connectivity between brain regions due to its relatively poor spatial resolution, extracting its statistical features may be helpful in the analysis of synchronization of brain oscillations. In this paper, a novel framework for the prediction of seizure occurrence using EEG signals is proposed which utilizes the Granger causality approach in frequency domain to measure synchronization of EEG signals in the Inter-ictal and Pre-ictal time periods. Afterwards, a Logistic Regression classifier with Lasso regularization is used to discriminate the samples extracted from these two periods. At last, if a predefined number of consecutive samples are labled as Pre-ictals, a seizure occurrence alarm is issued. Experimental simulations on the CHB-MIT dataset resulted in 95.03% sensitivity and 0.14/hour false prediction rate, for 10min prediction horizon, which demonstrates effectiveness of our proposed method compared to the state-of-the-arts.


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