C. Seijas, A. Caralli and S. Villazana, “Estimation of Brain Activity using Support Vector Machines,” Proceedings of the 3rd IEEE EMBS International Conference on Neural Engineering, Hawaii, 2-5 May 2007, pp. 604-607.

has been cited by the following article:

• TITLE: Neuropathology Classifier Based on Higher Order Spectra

  AUTHORS: Cesar Seijas, Antonino Caralli, Sergio Villazana

  KEYWORDS: Higher Order Spectra; Classification; Support Vector Machines; EEG; Epilepsy

  JOURNAL NAME: Journal of Computer and Communications, Vol.1 No.4, October 29, 2013

  ABSTRACT: Epilepsy is the most common neuropathology. Statistical studies related to the disease reported that 20% - 25% of epileptic patients with occurrence of seizures were even under treatment with drugs. This article presents a strategy for improved detection of the neuropathology, based on electroencephalogram (EEG), using a classifier built with support vector machines (SVC). The SVC is designed based on feature extraction of higher order spectra of time series derived from the EEG applied to epileptic patients and control patients. As demonstrated in the study presented, the EEG time series are highly nonlinear and non-Gaussian, therefore, exhibit higher order spectra, which are extracted features that improve the accuracy in the performance of SVC. The results of this study suggest the development of highly accurate computational tools for the diagnosis of this dreaded neuropathology.