Multichannel dynamic modeling of non-Gaussian mixtures. (September 2019)

• Record Type: Journal Article
• Authors: Safont, Gonzalo; Salazar, Addisson; Vergara, Luis; Gómez, Enriqueta; Villanueva, Vicente
• Is Part Of: Pattern recognition. Volume 93(2019:Sep.)
• Page Start: 312
• Page End: 323
• Publication Date: 2019-09
• Subjects: Dynamic modeling -- Non-Gaussian mixtures -- ICA -- HMM -- EEG; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.04.022
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A non-Gaussian mixture based method for dynamic modeling is proposed. Comparison with Gaussian mixtures and coupled hidden Markov models. Classification results outperform competitive methods on simulated and real data. Performance was measured using balanced error rate and Kappa index. Meaningful patterns were extracted from EEG signals during neuropsychological tests. Abstract: This paper presents a novel method that combines coupled hidden Markov models (HMM) and non-Gaussian mixture models based on independent component analyzer mixture models (ICAMM). The proposed method models the joint behavior of a number of synchronized sequential independent component analyzer mixture models (SICAMM), thus we have named it generalized SICAMM (G-SICAMM). The generalization allows for flexible estimation of complex data densities, subspace classification, blind source separation, and accurate modeling of both local and global dynamic interactions. In this work, the structured result obtained by G-SICAMM was used in two ways: classification and interpretation. Classification performance was tested on an extensive number of simulations and a set of real electroencephalograms (EEG) from epileptic patients performing neuropsychological tests. G-SICAMM outperformed the following competitive methods: Gaussian mixture models, HMM, Coupled HMM, ICAMM, SICAMM, and a long short-term memory (LSTM) recurrent neural network. As for interpretation, the structured result returned by G-SICAMM on EEGs was mapped back onto the scalp, providing a set of brain activations. These activations were consistent with the physiological areas activated during the tests, thus proving the ability of the method to deal with different kind of data densities and changing non-stationary and non-linear brain dynamics.