Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

NeuroImage ◽  
2010 ◽  
Vol 49 (1) ◽  
pp. 257-271 ◽  
Author(s):  
Aapo Hyvärinen ◽  
Pavan Ramkumar ◽  
Lauri Parkkonen ◽  
Riitta Hari
Keyword(s):  
Independent Component Analysis ◽  
Fourier Transforms ◽  
Component Analysis ◽  
Independent Component ◽  
Short Time

scholarly journals Doppler flow phantom failure detection by combining empirical mode decomposition and independent component analysis with short time Fourier transform

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 185
Author(s):  
Giorgia Fiori ◽  
Fabio Fuiano ◽  
Andrea Scorza ◽  
Maurizio Schmid ◽  
Silvia Conforto ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Component Analysis ◽  
Independent Component ◽  
Short Time Fourier Transform ◽  
Doppler Flow ◽  
Mode Decomposition ◽  
Flow Phantom ◽  
Short Time

<p class="Abstract">Nowadays, objective protocols and criteria for the monitoring of phantoms failures are still lacking in literature, despite their technical limitations. In such a context, the present work aims at providing an improvement of a previously proposed method for the Doppler flow phantom failures detection. Such failures were classified as low frequency oscillations, high velocity pulses and velocity drifts. The novel objective method, named EMoDICA-STFT, is based on the combined application of the Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Short Time Fourier Transform (STFT) techniques on Pulsed Wave (PW) Doppler spectrograms. After a first series of simulations and the determination of adaptive thresholds, phantom failures were detected on real PW spectrograms through the EMoDICA-STFT method. Data were acquired from two flow phantom models set at five flow regimes, through a single ultrasound (US) diagnostic system equipped with a linear, a convex and a phased array probe, as well as with two configuration settings. Despite the promising outcomes, further studies should be carried out on a greater number of Doppler phantoms and US systems as well as including an in-depth investigation of the proposed method uncertainty.</p>

Separation and Recognition of Electroencephalogram Patterns Using Temporal Independent Component Analysis

2015 ◽  
Vol 29 (01) ◽  
pp. 1550001 ◽  
Author(s):  
Ye Liu ◽  
Mingfen Li ◽  
Yi Wu ◽  
Jie Jia ◽  
Liqing Zhang
Keyword(s):  
Independent Component Analysis ◽  
Time Window ◽  
Component Analysis ◽  
Independent Component ◽  
Separation Performance ◽  
Eeg Analysis ◽  
Common Spatial Pattern ◽  
Classification Rate ◽  
Short Time ◽  
Source Signals

A common problem in Electroencephalogram (EEG) analysis is how to separate EEG patterns from noisy recordings. Independent component analysis (ICA), which is an effective method to recover independent sources from sensor outputs without assuming any a priori knowledge, has been widely used in such biological signals analysis. However, when dealing with EEG signals, the mixing model usually does not satisfy the standard ICA assumptions due to the time-variable structures of source signals. In this case, EEG patterns should be precisely separated and recognized in a short time window. Another issue is that we usually over-separate the signals by ICA due to the over learning problem when the length of data is not sufficient. In order to tackle these problems mentioned above, we try to exploit both high order statistics and temporal structures of source signals under condition of short time windows. We utilize a temporal-independent component analysis (tICA) method to formulate the blind separation problem into a new framework of analyzing the mutual independence of the residual signals. Furthermore, in order to find better features for classification, both temporal and spatial features of EEG recordings are extracted by integrating tICA together with some other algorithm like Common Spatial Pattern (CSP) for feature extraction. Computer simulations are given to evaluate the efficiency and performance of tICA based on EEG data recorded not from the normal people but from some special populations suffering from neurophysiological diseases like stroke. To the best of our knowledge, this is the first time that EEG characteristics of stroke patients are explored and reported using ICA algorithm. Superior separation performance and high classification rate evidence that the tICA method is promising for EEG analysis.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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