Independent Component Analysis for Source Localization of EEG Sleep Spindle Components. (14th March 2010)

• Record Type: Journal Article
• Authors: Ventouras, Erricos M.; Ktonas, Periklis Y.; Tsekou, Hara; Paparrigopoulos, Thomas; Kalatzis, Ioannis; Soldatos, Constantin R.
• Other Names: Gonzalez Andino Sara Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2010(2010)
• Publication Date: 2010-03-14
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2010/329436
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle "components" (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.