2013
Vazquez, Manuel A; Jin, Jing; Dauwels, Justin; Vialatte, Francois B
Automated Detection of Paroxysmal Gamma Waves in Meditation EEG Proceedings Article
En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1192–1196, IEEE, Vancouver, 2013, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation
@inproceedings{Vazquez2013,
title = {Automated Detection of Paroxysmal Gamma Waves in Meditation EEG},
author = {Manuel A Vazquez and Jing Jin and Justin Dauwels and Francois B Vialatte},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6637839},
issn = {1520-6149},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {1192--1196},
publisher = {IEEE},
address = {Vancouver},
abstract = {Meditation is a fascinating topic, yet has received limited attention in the neuroscience and signal processing community so far. A few studies have investigated electroencephalograms (EEG) recorded during meditation. Strong EEG activity has been observed in the left temporal lobe of meditators. Meditators exhibit more paroxysmal gamma waves (PGWs) in active regions of the brain. In this paper, a method is proposed to automatically detect PGWs from meditation EEG. The proposed algorithm is able to identify multiple sources in the brain that generate PGWs, and the sources associated with different types of PGWs can be distinguished. The effectiveness of the proposed method is assessed on 3 subjects possessing different degrees of expertise in practicing a yoga type meditation known as Bhramari Pranayama.},
keywords = {automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
Montoya-Martinez, Jair; Artés-Rodríguez, Antonio; Hansen, Lars Kai; Pontil, Massimiliano
Structured Sparsity Regularization Approach to the EEG Inverse Problem Proceedings Article
En: 2012 3rd International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Baiona, 2012, ISBN: 978-1-4673-1878-5.
Resumen | Enlaces | BibTeX | Etiquetas: BES, brain electrical sources matrix, Brain modeling, EEG inverse problem, Electrodes, Electroencephalography, good convergence, Inverse problems, large nonsmooth convex problems, medical signal processing, optimisation, Optimization, proximal splitting optimization methods, Sparse matrices, spatio-temporal source space, structured sparsity regularization approach, undetermined ill-posed problem
@inproceedings{Montoya-Martinez2012,
title = {Structured Sparsity Regularization Approach to the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Lars Kai Hansen and Massimiliano Pontil},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6232898},
isbn = {978-1-4673-1878-5},
year = {2012},
date = {2012-01-01},
booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Baiona},
abstract = {Localization of brain activity involves solving the EEG inverse problem, which is an undetermined ill-posed problem. We propose a novel approach consisting in estimating, using structured sparsity regularization techniques, the Brain Electrical Sources (BES) matrix directly in the spatio-temporal source space. We use proximal splitting optimization methods, which are efficient optimization techniques, with good convergence rates and with the ability to handle large nonsmooth convex problems, which is the typical scenario in the EEG inverse problem. We have evaluated our approach under a simulated scenario, consisting in estimating a synthetic BES matrix with 5124 sources. We report results using ℓ1 (LASSO), ℓ1/ℓ2 (Group LASSO) and ℓ1 + ℓ1/ℓ2 (Sparse Group LASSO) regularizers.},
keywords = {BES, brain electrical sources matrix, Brain modeling, EEG inverse problem, Electrodes, Electroencephalography, good convergence, Inverse problems, large nonsmooth convex problems, medical signal processing, optimisation, Optimization, proximal splitting optimization methods, Sparse matrices, spatio-temporal source space, structured sparsity regularization approach, undetermined ill-posed problem},
pubstate = {published},
tppubtype = {inproceedings}
}