DocumentCode :
1656102
Title :
Automated detection of paroxysmal gamma waves in meditation EEG
Author :
Vazquez, Miguel Angel ; Jing Jin ; Dauwels, Justin ; Vialatte, Francois B.
Author_Institution :
Depto. de Teor. de la Senal y Comun., Univ. Carlos III de Madrid, Madrid, Spain
fYear :
2013
Firstpage :
1192
Lastpage :
1196
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 :
electroencephalography; medical signal detection; neurophysiology; Bhramari Pranayama; EEG activity; PGW; automated detection; brain active region; brain multiple source identification; electroencephalogram; left temporal lobe; meditation EEG; meditator; neuroscience; paroxysmal gamma waves; signal processing community; yoga type meditation; Blind source separation; Detectors; Electroencephalography; Principal component analysis; Sensitivity; Temporal lobe; Bhramari Pranayama; Electroencephalogram; Meditation; Paroxysmal gamma wave; Spike detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2013.6637839
Filename :
6637839
Link To Document :
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