Imaging brain dynamics using independent component analysis

Jung, Tzyy‐Ping; Makeig, Scott; McKeown, Martin J.; Bell, Anthony J.; Lee, T.-W.; Sejnowski, Terrence J.

doi:10.1109/5.939827

Cited by 504 publications

(323 citation statements)

References 59 publications

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“…Independent Component Analysis (ICA) (Bell and Sejnowski, 1995;Jung et al, 2001; Makeig et al, 1996) applied to concatenated collections of single-trial EEG data has also proven to be an efficient method for separating distinct artifactual processes including eye blink, muscle, and electrical artifacts (Barbati et . Although several ICA algorithms in different implementations have been used to separate artifacts from EEG and MEG data, they all can be derived from related mathematical principles (Lee et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

2007

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Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (−50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICAdecomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed nonartifact processes themselves.

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Section: Introductionmentioning

confidence: 99%

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

2007

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“…Note that this application assumes the fulfillment of the ICA model, hence, it will only be possible to derive the spatial filters (mixing matrix) and the sources from the ECG, when the physical sources associated to heart's activity are spatially stationary [1]. Contraction of the atria during fibrillation, the ventricles in the cardiac cycle or any other relative movement from sources to observations, could violate the assumption of spatial stationarity.…”

Section: Resultsmentioning

confidence: 99%

“…One of the most important research areas where independent component analysis (ICA) techniques have proved their success is in biomedical engineering [1], with a relevant increase of novel applications during the past years. Regarding the electrocardiogram (ECG), it is well known the extraction of the fetal ECG from maternal recordings [2], the separation of breathing artifacts and other disturbances [3], analysis of ST segments for ischemia detection [4], identification of humans using the ECG [5], ventricular arrhythmia detection and classification [6] and the study of atrial fibrillation (AF).…”

Section: Introductionmentioning

confidence: 99%

Mixing Matrix Pseudostationarity and ECG Preprocessing Impact on ICA-Based Atrial Fibrillation Analysis

Rieta¹,

Sánchez²,

Sanchis³

et al. 2004

Independent Component Analysis and Blind Signal Separation

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Abstract. In this work two relevant considerations in the ICA-based estimation of atrial activity (AA) in atrial fibrillation (AF) episodes from real electrocardiogram (ECG) recordings are presented. Firstly, the impact of low-pass filtering preprocessing on the extraction quality of AA is analyzed, showing an average improvement over 17% in spectral concentration (SC) when low-pass filtering is applied after ICA with respect to the application of the same filtering before ICA. Secondly, it is demonstrated that the ICA mixing matrix obtained from one AF segment can also be used to estimate the AA present in different segments of the same recording, thus proving the pseudostationarity of the mixing matrix. Results over 32 AF segments show a mean cross-correlation of R dp = 81.5% between the directly estimated AA and the estimated using presudostationarity. Changes in spectral concentration from one case to the other (∆SC dp = 1.4%) are negligible.

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“…The first one is related to blind source separation of images and the second one for RF photonics. Regarding blind source separation (BSS) there are many approaches, such as Principal Component Analysis (PCA), which uses second order statistics and decorrelates the outputs by using an orthogonal demixing matrix [23][24][25], algorithm that recovers images on the pixel-by-pixel basis [26] and approaches based upon adaptive filter that performs image separation in the ZALEVSKY et al…”

Section: General Applicationsmentioning

confidence: 99%

Fractional Fourier transform-exceeding the classical concepts of signal’s manipulation

2007

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-The fractional Fourier transform is a signal processing tool which is strongly associated with optical data manipulation. It has fast computational algorithms and it suggests solutions to interesting signal processing tasks. In this paper we review its properties as well as present a new set of its applications for blind source separation of images and for RF photonics (a field in which photonic devices are used to process RF signals).

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Imaging brain dynamics using independent component analysis

Abstract: The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG)

Cited by 504 publications

References 59 publications

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Mixing Matrix Pseudostationarity and ECG Preprocessing Impact on ICA-Based Atrial Fibrillation Analysis

Fractional Fourier transform-exceeding the classical concepts of signal’s manipulation

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