@inproceedings{8c746d04fb144d3ebc335e52e8334b7d,
title = "Long-term scalp epileptic EEG quantification with GMA dynamics",
abstract = "The paper concerns the problem of automatic seizure detection based on scalp EEG and proposes to employ the generalized measure of association (GMA) to quantify the statistical dependencies and infer the dynamical interactions of brain regions with the focus area. The experimental results with clinical recordings show that the estimated GMA values changes dramatically before and during epileptic seizures reflecting the dynamic coupling and decoupling between brain regions, which can be an useful measure to quantify epileptic EEG signals.",
author = "Hong Ji and Hazrati, \{Mehrnaz Kh\} and Badong Chen and Yonghong Liu and Andreas Keil and Principe, \{Jose C.\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 ; Conference date: 25-08-2015 Through 29-08-2015",
year = "2015",
month = nov,
day = "4",
doi = "10.1109/EMBC.2015.7318996",
language = "英语",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2892--2895",
booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015",
}