@conference {2849,
	title = {Phase synchronization with ICA for epileptic seizure onset prediction in the long term EEG.},
	booktitle = {4th IET International Conference on Advances in Medical, Signal and Information Processing},
	year = {2008},
	month = {07/2008},
	publisher = {IET},
	organization = {IET},
	address = {Santa Margherita Ligure},
	abstract = {The apparently unpredictable nature of epileptic seizures can be devastating for people with epilepsy. Current medical interventions can help 75\% of patients while 25\% have to live with uncontrolled seizures. This motivates the search for a seizure prediction prototype using electroencephalograms (electrical signals that capture brain activity). The concept of phase synchrony has attracted much attention recently in the context of seizure prediction but is still in need of further study. The basis of our analysis is to track changes in synchrony in brain signals at and before seizure onset. The novel concept in our analysis is the use of unmixed signals as opposed to scalp EEG signals for phase synchrony analysis. The unmixing is performed by a Blind Source Separation technique called Independent component Analysis (ICA). ICA seeks underlying independent source signals from the EEG and it allows multivariate analysis using spatial as well as temporal information inherent to EEG signals. The present study on long-term continuous EEG data sets indicates that the concept of using phase synchronization with ICA may prove useful for predicting seizures.},
	isbn = {978-0-86341-934-8},
	url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4609101\&abstractAccess=no\&userType=inst},
	author = {Disha Gupta and Christopher J James and William P Gray}
}
@conference {2848,
	title = {De-noising epileptic EEG using ICA and phase synchrony.},
	booktitle = {3rd International Conference on Advances in Medical, Signal and Information Processing, IET},
	year = {2006},
	month = {07/2006},
	publisher = {IET, Curran Associates, Inc.},
	organization = {IET, Curran Associates, Inc.},
	address = {Glasgow, Scotland},
	abstract = {A multi-channel recording of scalp electroencephalogram (EEG) is a non-invasive tool important for analysis and treatment of patients with epilepsy. These recordings are usually contaminated with artifacts and background activity, which may sometimes render them misleading or useless. Epileptic EEG is also useful for seizure detection, localisation and prediction. It would be useful to de-noise epileptic EEG in order to improve the efficiency of such diagnostic and prognostic procedures. The basic method of denoising a signal is through filtering, but filtering physiological signals is not trivial and highly subjective as the information is spread over different frequency bands and different measurement channels. This paper demonstrates a system for objectively de-noising epileptic EEG using Independent Component Analysis (ICA). In the standard implementation of ICA it is generally required to subjectively choose independent components (ICs) relevant to the epileptic activity; here we automate this process through the concept of phase synchronisation between ICs. In this manner de-noising the epileptic EEG with ICA becomes an objective (and automated) process.},
	isbn = {978-0-86341-658-3},
	doi = {10.1049/cp:20060398},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4225235},
	author = {Disha Gupta and Christopher J James and William P Gray}
}
