Current Source Reconstruction with Independent Component Analysis in Sensor Array Measurement of Multi-Conductor Current

In this paper, we present a space invariant architecture to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources. The two sets of unknown paired mixture sources are collected via JPL 16-ENose sensor array in the unknown environment with, at most, 12 samples data collected. Our space invariant architecture along with the maximum entropy information technique by Bell and Sejnowski and natural gradient descent by Amari has demonstrated that it is effective to separate the two mixing unknown chemical sources with unknown mixing levels to the array of two original sources under insufficient sampled data. From separated sources, they can be identified by projecting them on the 11 known chemical sources to find the best match for detection. We also present the results of our simulations. These simulations have shown that 100% correct detection could be achieved under the two cases: a) under-completed case where the number of input (mixtures) is larger than number of original chemical sources; and b) regular case where the number of input is as the same as the number of sources while the time invariant architecture approach may face the obstacles: overcomplete case, insufficient data and cumbersome architecture.

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Mixed Vibration Signal Separation and Moving Object Detection Based on Independent Component Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.328-330.2113 ◽

2011 ◽

Vol 328-330 ◽

pp. 2113-2116

Author(s):

Ning Qiang ◽

Fang Xiang

Keyword(s):

Independent Component Analysis ◽

Sensor Array ◽

Vibration Signal ◽

Component Analysis ◽

Spatial Spectrum ◽

Independent Component ◽

Signal Separation ◽

Moving Targets ◽

Spectrum Function ◽

Multiple Moving Targets

This article briefly describes the basic theory of independent component analysis (ICA) and algorithms. Independent component analysis (ICA) method is employed to separate the mixed vibration signal, measured from linear sensor array. By calculating the spatial spectrum function, identification and tracking of multiple moving targets achieved. The results show that, ICA can successfully detect and track multiple targets.

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Independent Component Analysis for Source Localization of EEG Sleep Spindle Components

Computational Intelligence and Neuroscience ◽

10.1155/2010/329436 ◽

2010 ◽

Vol 2010 ◽

pp. 1-12 ◽

Cited By ~ 25

Author(s):

Erricos M. Ventouras ◽

Periklis Y. Ktonas ◽

Hara Tsekou ◽

Thomas Paparrigopoulos ◽

Ioannis Kalatzis ◽

...

Keyword(s):

Independent Component Analysis ◽

Visual Analysis ◽

Activity Patterns ◽

Current Source ◽

Component Analysis ◽

Independent Component ◽

Source Analysis ◽

Sleep Spindles ◽

Electroencephalogram Eeg ◽

Selection Of

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.

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