Independent Component Analysis: Applications to Biomedical Signal Processing

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Applications of Independent Component Analysis to Biomedical Signal Processing


What is Independent Component Analysis (ICA)?


Overview

The goal of blind source separation in signal processing is to recover independent source signals (e.g., different people speaking, music etc.) after they are linearly mixed by an unknown medium, and recorded at N sensors (e.g., microphones). The blind source separation problem has been studied by researchers in the neural network (Bell & Sejnowski, 1995; Amari et al, 1996, Cichocki et al., 1994; Girolami & Fyfe, 1996; Karhunen et al, 1996, Pearlmutter & Parra, 1996; Roth & Baram, 1996) and statistical signal processing communities (Cardoso & Laheld, 1996; Comon, 1994; Lambert, 1996; Pham,1996; Yellin & Weinstein, 1996). Comon (1994) defined the concept of independent component analysis (ICA) as maximizing the degree of statistical independence among outputs using contrast functions approximated by the Edgeworth expansion of the Kullback-Leibler divergence. In contrast with decorrelation techniques such as Principal Component Analysis (PCA), which ensures that output pairs are uncorrelated. ICA imposes the much stronger criterion that the multivariate probability density function (p.d.f.) of output variables factorizes. Finding such a factorization requires that the mutual information between all variable pairs go to zero. Mutual information depends on all higher-order statistics of the output variables while decorrelation only takes account of 2nd-order statistics.

Recently, Bell and Sejnowski (1995) have proposed a simple neural network algorithm or InfoMax algorithm, for carrying out ICA. For details about the algorithm, Matlab package, and paper collection, please visit ICA-CNL page.

Currently, I am applying several ICA algorithms to the analysis of EEG, ERP, and fMRI data to open wider windows for non-invasive observation of cognitive brain dynamics. The underlying assumptions and theoretical and practical questions regarding applying ICA to biomedical signals are addressed in Frequently Asked Questions about ICA applied to EEG/MEG data.


Recent Publications

Abstracts

  1. Bartlett M, Makeig S, Bell AJ, Jung T-P, Sejnowski TJ, "Independent Component Analysis of EEG Data", Society for Neuroscience Abstracts, 21:437, 1995.
  2. Makeig S, Jung T-P, Ghahremani D, Bell A, Sejnowski TJ, "What (Not Where) are the Sources of the EEG?", , Proc. The 18th Annual Meeting of The Cognitive Science Society 802. 1996.
  3. Makeig S, Anllo-Vento L, Jung T-P, Bell AJ, Sejnowski TJ, Hillyard SA "Independent Component Analysis of event-related potentials during selective attention", Society for Neuroscience Abstracts, 22:1698, 1996.
  4. Makeig S, Jung T-P, Bell AJ, Ghahremani D, and Sejnowski TJ, "Blind separation of event-related brain response components", Society for Psychophysiological Research, Abstract, Oct. 1996.
  5. Makeig S, Anllo-Vento L, Jung T-P, Bell AJ, Sejnowski TJ, and Hillyard SA, " Independent component analysis of event-related potentials during a selective attention task.", Society for Psychophysiological Research, Abstract, Oct. 1996.
  6. McKeown MJ, Makeig S, Jung T-P, Brown GG, Kindermann SS, and Sejnowski TJ, "Analysis of fMRI Data by Decomposition into Independent Components", American Academy of Neurology Abstract, 48, A417, 1997.
  7. McKeown MJ, Makeig S, Jung T-P, Brown GG, Kindermann SS, and Sejnowski TJ, "Functional Magnetic Resonance Imaging Data Interpreted As Spatially Independent Mixtures'', Proc. 4th Joint Symposium on Neural Computation, May 1997.
  8. Makeig S, Jung T-P , Lee T-W, and Sejnowski TJ, "Independent Component Analysis of Steady-state Responses", The Annual Meeting of Society for Psychophysiological Research, Oct. 1997.
  9. McKeown MJ, Makeig S, Jung T-P, Brown GG, Kindermann SS, and Sejnowski TJ, "Finding the Time Courses of Spatially Independent Activations in fMRI Data during STROOP and Control Tasks", Society for Neuroscience Abstracts, Oct. 1997.
  10. Makeig S, Jung T-P, and Sejnowski TJ, "Independent Component Analysis of Single-trial Event-related Potentials", Society for Neuroscience Abstracts, Oct. 1997.
  11. Jung T-P, Makeig S, and Sejnowski TJ, "Identifying and Visualizing Independent Components in Artifact-Free Single-trial Event-Related Potentials", Society for Neuroscience Abstracts, Oct. 1998.
  12. Makeig S, Jung T-P and Sejnowski TJ , "Multiple Coherent Oscillatory Components of the Human Electroencephalogram (EEG) Differently Modulated by Cognitive Events'', Society for Neuroscience Abstracts , Oct. 1998.
  13. Jung T-P, Makeig S, Townsend J, Westerfield W, Hicks B, Courchesne E,and Sejnowski TJ, "Single-trial ERPs durinf Continuous fMRI Scanning", Society for Neuroscience Abstracts , Oct. 1999.
  14. Makeig S, Townsend J, Jung T-P, Enghoff A, Gibson C, and Sejnowski TJ, "Early Visual Evoked Response Peaks Appear to be Sums of Activity in Multiple Alpha Sources", Society for Neuroscience Abstracts , Oct. 1999.
  15. Makeig S, Jung T-P and Sejnowski TJ, "Indepednent Components of event-related brain dynamics", Society for Psychophysiology Research Abstracts , Sep. 1999.
  16. Edwards E, Townsend J, Westerfield M, Makeig S, Jung T-P, "Cortical lesions affect the late positive potential during visual-spatial attention" Cognitive Neuroscience Society Abstracts, p.90, April 2000.
  17. Makeig S, Enghoff S, Jung T-P, and Sejnowski TJ, "Independent Components of Event-related Electroencephalographic Data", Cognitive Neuroscience Society Abstracts, p.93, April 2000.
  18. Jeng-Ren Duann, T.P. Jung, W.J. Kuo, T.C. Yeh, S. Makeig, J.C. Hsieh, T.J. Sejnowski. Measuring variability of hemodynamics responses in event-related BOLD data using ICA. The 8th Joint Symposium on Neural Computation, The Salk Institute, La Jolla CA, May 19, 2001
  19. Marissa Westerfield , Scott Makeig, T-P. Jung, T. J. Sejnowski, Independent Component Analysis reveals the spatial sources of the visual P300. The 8th Joint Symposium on Neural Computation, Institute for Neural Computation Symposium, The Salk Institute, La Jolla CA, May 19, 2001.
  20. Scott Makeig, Tzyy-Ping Jung, Marissa Westerfield and Terrence J. Sejnowski, Imaging event-related brain dynamics, Human Brain Mapping 2001, Brighton, England, June 10-14, 2001.

Articles

  1. Makeig S, Bell AJ, Jung T-P, and Sejnowski TJ, "Independent component analysis of Electroencephalographic data." Advances in Neural Information Processing Systems 8, 145-151,1996.
  2. Ghahremani D, Makeig S, Jung T-P, Bell AJ, Sejnowski TJ, "Independent component analysis of simulated EEG using a three-shell spherical head model", Tech Rep. INC-9601, Institute for Neural Computation, University of California, San Diego, May 1996.
  3. Makeig S, Jung T-P, Ghahremani D and Sejnowski TJ, Independent Component Analysis of Simulated ERP Data, Tech Rep. INC-9606, Institute for Neural Computation, University of California, San Diego, 1996.
  4. Jung T-P, Makeig S, Bell AJ, and Sejnowski TJ, "Independent Component Analysis of Electroencephalographic and Event-related Data." In: (Poon P. and Brugge J, ed) Auditory Processing and Neural Modeling , 189-197, 1998.
  5. Makeig S, Jung T-P, Bell AJ, and Sejnowski TJ, "Blind Separation of Auditory Event-related Brain Responses into Independent Components," Proc. of National Academy of Sciences, 94:10979-84, 1997.
  6. McKeown MJ, Jung T-P, Makeig S, Brown GG, Kindermann G, Lee T-W, and Sejnowski TJ, "Spatially Independent Activity Patterns in Functional Magnetic Resonance Imaging Data During the Stroop Color-naming Task," Proc. of National Academy of Sciences, 95:803-10, 1998.
  7. Jung T-P, Humphries C, Lee TW, Makeig S, McKeown MJ, Iragui V, and Sejnowski TJ, "Extended ICA Removes Artifacts from Electroencephalographic Recordings", Advances in Neural Information Processing Systems 10:894-900, 1998
  8. McKeown MJ, Makeig S, Brown GG, Jung T-P, Kindermann SS, and Sejnowski TJ, "Analysis of fMRI by Blind Separation into Independent Spatial Components", Human Brain Mapping, 6(3):160-88, 1998.
  9. Jung T-P, Humphries C, Lee TW, Makeig S, McKeown MJ, Iragui V, and Sejnowski TJ, "Removing Electroencephalographic Artifacts : Comparison between ICA and PCA", In: Neural Networks for Signal Processing VIII, 63-72.
  10. Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, and Sejnowski TJ, "Independent Component Analysis of Single-trial Evenet-related Potentials," Int'l Workshop on Indeppendent Component Analysis and Signal Separation, 173-178, 1999.
  11. Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, and Sejnowski TJ, "Analyzing and Visualizing Single-trial Event-related Potentials," Advances in Neural Information Processing Systems, 11:118-24, 1999.
  12. Makeig S, Westerfield M, Jung T-P, Covington J, Townsend J, Sejnowski TJ, and Courchesne E, "Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention," J. Neuroscience, 19: 2665-80, 1999.
  13. Makeig S, Westerfield M, Townsend J, Jung T-P, Courchesne E and Sejnowski TJ, "Functionally Independent Components of Early Event-Related Potentials in a Visual Spatial Attention Task," Philosophical Transactions: Biological Sciences, 354: 1135-44, 1999.
  14. Jung T-P, Humphries C , Lee TW, McKeown MJ, Iragui V, Makeig S, and Sejnowski TJ, "Removing Electroencephalographic Artifacts from by Blind Source Separation," Psychophysiology, 37: 163-78, 2000.
  15. Makeig S, Enghoff S, Jung T-P, and Sejnowski TJ, "A Natural Basis for Efficient Brain-Actuated Control", IEEE Trans Rehab Eng, 8:208-11, 2000.
  16. Makeig S, Enghoff S, Jung T-P, and Sejnowski TJ, "Moving-window ICA Decomposition of EEG Data Reveals Event-related Changes in Oscillatory Brain Activity," The 2nd Int'l Workshop on Indeppendent Component Analysis and Signal Separation, 627-32, 2000.
  17. Jung T-P, Makeig S, Lee T-W, McKeown M.J., Brown G., Bell, A.J. and Sejnowski TJ, "Independent Component Analysis of Biomedical Signals," The 2nd Int'l Workshop on Indeppendent Component Analysis and Signal Separation, 633-44, 2000.
  18. Jung T-P, Makeig S, Westerfield W, Townsend J, Courchesne E, and Sejnowski TJ, "Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects", Clinical Neurophysiology, 111(10):1745-58, 2000.
  19. Makeig S, Jung T-P, Ghahremani D and Sejnowski TJ, "Independent component analysis of simulated ERP data", In: Human Higher Function I: Advanced Methodologies," Ed. T. Nakada, 2001.
  20. Townsend, J., Westerfield, M., Leaver, E., Makeig, S., Jung, T-P, Pierce, K., Courchesne, Abnormalities of topography and composition of ERP response in autis during spatial attention, Cognitive Brain Research, 11(1), 127-145, 2001.
  21. Jung T-P, Makeig S, McKeown M.J., Bell, A.J. , Lee T-W, and Sejnowski TJ, Imaging Brain Dynamics Using Independent Component Analysis, Proceedings of the IEEE, 89(7):1107-22, 2001.
  22. Jung T-P, Makeig S, Westerfield W, Townsend J, Courchesne E, and Sejnowski TJ, Analysis and visualization of single-trial event-related potentials, Human Brain Mapping, 14(3):166-85, 2001.
  23. Makeig S, Westerfield M, Jung T-P, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ. Dynamic brain sources of visual evoked responses. Science, 295:690-4, Jan. 25, 2002.
  24. Duann J-R, Jung T-P, Kuo W-J, Yeh T-C, Makeig S, Hsieh J-C and Sejnowski TJ, Single-trial Variability in Event-related BOLD Signals, Neuroimage, 15:823-35, 2002.
  25. Westerfield M, Townsend J, Jung T-P, Courchesne E, "The Endogenous ERP is Reduced Cerebellar Damage, Especially Over Frontal Cortex," submitted.


Independent Component Analysis of EEG Data

Marian Stewart Bartlett, Scott Makeig, Anthony J. Bell, Tzyy-Ping Jung, and Terrence J. Sejnowski
Society for Neuroscience Abstracts, 21:437, 1995.

Abstract

Because of the spread of electromagnetic signals through CSF and skull through volume conduction, EEG data recorded at different points on the scalp tend to be correlated. Bell and Sejnowski (1995) have recently presented an artificial neural network algorithm that identifies and separates statistically independent signals from a number of channels composed of linear mixtures of an equal number of sources. Here we present a first application of this Independent Component Analysis (ICA) algorithm to human EEG data. Conceptually, ICA filtering separates the problem of source identification in EEG data from the related problem of physical source localization. Three subjects performed a continuous auditory detection task in two half hour sessions. ICA filters trained on 14-channel EEG data collected during these sessions identified 14 statistically independent source channels which could then be further processed using event-related potential (ERP), event-related spectral perturbation (ERSP), and other signal processing techniques. One ICA source channel contained most eye movement activity, and another two collected line noise and muscle activity, while others were free of these artifacts. Changes in spectral power in several ICA channels covaried with changes in performance. If ICA sources can be shown to have distinct and consistent relationships to behavior or other physiological signals, ICA filtering may reveal meaningful aspects of event-related brain dynamics associated with sensory and cognitive processing but hidden within correlated EEG responses at individual scalp sites.

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What (Not Where) are the Sources of the EEG?

Scott Makeig, Tzyy-Ping Jung, Dara Ghahremani, Anthony J. Bell, Terrence J. Sejnowski,
The 18th Annual Meeting of The Cognitive Science Society, Jul. 1996.

Abstract

The problem of determining brain electrical sources from potential patterns recorded on the scalp surface is mathematically underdetermined. We have applied the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski to the problem of source identification (What) considered apart from source localization (Where). By maximizing the joint entropy of a set of output channels derived from input signals by linear filtering without time delays, the ICA algorithm attempts to derive independent sources from highly correlated scalp EEG signals without regard to the locations or configurations (focal or diffuse) of the source generators. We report simulation experiments to determine (1) whether the ICA algorithm can successfully isolate independent components in simulated EEG generated by focal and distributed sources, and (2) whether ICA performance is severely affected by sensor noise and additional low-level brain noise sources. We will also show examples of ICA applied to actual EEG and cognitive ERP data.

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Independent Component Analysis of Event-related Potentials during Selective Attention

Scott Makeig, L. Anllo-Vento, Tzyy-Ping Jung, Anthony J. Bell, Terrence J. Sejnowski, and Steven A. Hillyard, Society for Neuroscience Abstracts, 22:1698, 1996.

Abstract

Recordings of event-related potentials (ERPs) can reveal the time course of brain events associated with visual perception and selective attention. ERP studies of visual-spatial attention indicate that cortical processing of stimuli appearing in the attended location is augmented as early as 80 ms after stimulus onset. However, separation of the multiple brain processes contributing to the surface-recorded components of ERP waveforms has proven difficult. Recently, an `infomax' algorithm for the blind separation of linearly mixed inputs has been devised (Bell and Sejnowski, 1995) and applied to EEG and ERP analysis (Makeig et al., 1996). The neural generators of ICA sources are not specified by the algorithm and may be either physically compact or distributed.

Results of applying this Independent Component Analysis (ICA) algorithm to single-subject and group-mean ERPs recorded during a visual selective attention experiment (Anllo-Vento and Hillyard, 1996) suggest that ERP waveforms represent a sum of overlapping, discrete and time-limited brain processing events whose amplitudes are modulated by selective attention without affecting their time course. These source components identified by ICA appear to index independent stages of visual information processing. Spatial attention operates on early source components in a manner similar to a sensory gain-control mechanism, while later components appear to reflect further processing of stimulus features and feature conjunctions.

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Blind Separation of Event-related Brain Response Components

Scott Makeig, Tzyy-Ping Jung, Anthony J. Bell, Dara Ghahremani, Terrence J. Sejnowski,
Society for Psychophysiological Research, Oct. 1996.

Abstract

The problem of objectively decomposing event-related brain responses into neurophysiologically meaningful components is a major difficulty in the evoked response field. Traditional methods of identifying and measuring response subcomponents based on measuring the amplitudes and latencies of peak excursions in the waveforms at individual scalp sites fail when subcomponents overlap substantially, while current source localization procedures based on fitting single or multiple dipole models give ambiguous results when source geometry is unknown or complex. The Independent Component Analysis (ICA) algorithm of Bell and Sejnowski (1995) is an artificial neural network which maximizes the overall entropy of a set of non-linearly transformed input vectors using stochastic gradient ascent, without regard to the physical locations or configuration of the source generators. Trained on one or more multichannel electric or magnetic evoked responses, the algorithm converges on spatial filters which separate the input data into independent time courses and distinct scalp topographies arising in multiple, spatially-stationary 'effective brain sources.' Response decompositions produced by the ICA algorithm can be used to measure the effects of experimental manipulations on individual response components, even when these are overlapping in time or space. Typically, response components identified by the algorithm are recaptured in repeated analyses, regardless of changes in initial weights, sensor montage, and data length. I will explain the theory and practise of ICA decomposition and its differences from PCA, demonstrate results of EEG simulations, and present applications to EEG and MEG data analysis.

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Independent Component Analysis of Event-related Potentials during Selective Attention

Scott Makeig, L. Anllo-Vento, Tzyy-Ping Jung, Anthony J. Bell, Terrence J. Sejnowski, and Steven A. Hillyard, Society for Psychophysiological Research, Oct. 1996.

Abstract

Event-related potentials (ERPs) can reveal the time course of brain events associated with visual perception and selective attention. ERP studies of visual-spatial attention indicate that cortical processing of stimuli appearing in the attended location is augmented as early as 80 ms after stimulus onset. However, separation of the multiple brain processes contributing to the surface-recorded components of ERP waveforms has proven difficult. Recently, an `infomax' algorithm for the blind separation of linearly mixed inputs has been devised (Bell and Sejnowski, 1995) and applied to EEG and ERP analysis (Makeig et al., 1996). The neural generators of ICA sources are not specified by the algorithm and may be either physically compact or distributed. Results of applying this Independent Component Analysis (ICA) algorithm to single-subject and group-mean ERPs recorded during a visual selective attention experiment (Anllo-Vento and Hillyard, 1996) suggest that ERP waveforms represent a sum of overlapping, discrete and time-limited brain processing events whose amplitudes are modulated by selective attention without affecting their time course. These source components identified by ICA appear to index independent stages of visual information processing. Spatial attention operates on early source components in a manner similar to a sensory gain-control mechanism, while later components appear to reflect further processing of stimulus features and feature conjunctions.

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Independent Component Analysis of Electroencephalographic Data

Scott Makeig, Anthony J. Bell, Tzyy-Ping Jung and Terrence J. Sejnowski
In: D. Touretzky, M. Mozer and M. Hasselmo (Eds). Advances in Neural Information Processing Systems 8, 145-151, 1996.

Abstract

Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, suggesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski (Bell and Sejnowski, 1995) is suitable for performing blind source separation on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA analysis may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA analysis is capable of isolating overlapping alpha and theta wave bursts to separate ICA channels (4) Nonstationarities in EEG and behavioral state can be tracked using ICA analysis via changes in the amount of residual correlation between ICA-filtered output channels.

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Independent Component Analysis of Simulated EEG using a Three-shell Spherical Head Model

Dara Ghahremani, Scott Makeig, Tzyy-Ping Jung, Anthony J. Bell, Terrence J. Sejnowski,
Technical Report INC-9601, Institute for Neural Computation, University of California, San Diego, May 1996.

Abstract

The Independent Component Analysis (ICA) algorithm of Bell and Sejnowski (Bell and Sejnowski, 1995) is an information-theoretic unsupervised learning algorithm which can be applied to the problem of separating multichannel electroencephalographic (EEG) data into independent sources (Makeig et al., 1996). We tested the potential usefulness of the ICA algorithm for EEG source decomposition by applying the algorithm to simulated EEG data. This data was constructed by projecting known input signals from single- and multiple-dipole sources in a three-shell spherical model head (Dale and Sereno, 1993) to simulated scalp sensors.

In different simulations, we (1) altered the relative source strengths, (2) added multiple low-level sources (weak brain sources and sensor noise) to the simulated EEG, and (3) permuted the simulated dipole source locations and orientations. The algorithm successfully and reliably separated the activities of relatively strong sources from the activities of weaker brain sources and sensor noise, regardless of source locations and dipole orientations. These results suggest that the ICA algorithm should be able to separate temporally independent but spatially overlapping EEG activities arising from relatively strong brain and/or non-brain sources, irrespective of their spatial distributions.

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Independent Component Analysis of Simulated ERP Data

Scott Makeig, Tzyy-Ping Jung, Dara Ghahremani and Terrence J. Sejnowski, Technical Report INC-9606, Institute for Neural Computation, University of California, San Diego, 1996.

Abstract

A recently-derived 'infomax' algorithm for performing Independent Component Analysis (ICA) is a new information-theoretic approach to the problem of separating multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data into temporally independent and spatially stationary sources. In a previous report, we have shown that the algorithm can separate simulated EEG source waveforms (independent simulated brain source activities mixed linearly at the scalp sensors), even in the presence of multiple low-level model brain and sensor noise sources. Here, we demonstrate the ability of the ICA algorithm to decompose brief event-related potential (ERP) data sets into temporally independent components by applying it to simulated ERP-length EEG data synthesized from 3-sec (600-point) electrocorticographic (ECoG) epochs recorded from the cortical surface of a human undergoing pre-surgical evaluation.

Six asynchronous single-channel ECoG data epochs were projected through single- and multiple-dipole model sources in a three-shell spherical head model to six simulated scalp sensors to create simulated EEG data. In two sets of simulation experiments, we altered relative source strengths, added multiple low-level sources (synthesized from ECoG data and uniform- or Gaussian-distributed noise), and permuted the simulated dipole source locations and orientations. The algorithm reliably separated the activities of the relatively strong sources, regardless of source location, dipole orientation, and low-level source distributions. Thus, the ICA algorithm should identify relatively strong, temporally independent and spatially overlapping ERP components arising from multiple brain and/or non-brain sources, regardless of their spatial distributions. This shows that the ICA algorithm can decompose ERPs generated by uncorrelated sources.

A third ERP simulation tests how the algorithm treats a simulated ERP epoch constructed using model ERP generators whose activations are partially correlated. In this case, the algorithm parsed the simulated ERP waveforms into a sum of temporally independent and spatially stationary components reflecting the changing topography of correlated source activity in the simulated ERP data. Each of the affected components sums activity from one or more concurrently-active brain generators. This suggests the ICA algorithm may also be useful for identifying event-related changes in the correlation structure of either spontaneous or event-related EEG data. Paradoxically, adding four simulated ``no response'' epochs to the training data minimized the relative importance of partial correlations in the original data epoch and allowed the algorithm to separate the concurrently active sources. Likewise, submitting ERPs from more than one stimulus or experimental condition to concurrent ICA analysis may allow the algorithm to separate sources from brain generators whose activations are partially correlated in some but not all response conditions.

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Blind Separation of Auditory Event-related Brain Responses into Independent Components

Scott Makeig, Tzyy-Ping Jung, Anthony J. Bell, Dara Ghahremani, Terrence J. Sejnowski,
Proc. of National Academy of Sciences, 94:10979-84, 1997.

Abstract

Averaged event-related potential (ERP) data recorded from the human scalp reveals electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely-activated, maximally independent time courses. Independent Component Analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected- and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This new method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

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Spatially Independent Activity Patterns in Functional Magnetic Resonance Imaging Data During the Stroop Color-naming Task

Martin J. McKeown, Tzyy-Ping Jung, Scott Makeig, Greg G. Brown, Kindermann, and Terrence J. Sejnowski
Proc. of National Academy of Sciences, 95:803-10, 1998.

Abstract

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a "map") and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

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Extended ICA Removes Artifacts from Electroencephalographic Recordings

Tzyy-Ping Jung, Colin Humphries, Te-Won Lee, Scott Makeig, Martin J. McKeown, Vicente Iragui, Terrence J. Sejnowski. In: D. Touretzky, M. Mozer and M. Hasselmo (Eds). Advances in Neural Information Processing Systems 10:894-900, 1998.

Abstract

Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, and muscle, heart and line noise presents a serious problem for EEG interpretation and analysis. Rejecting contaminated EEG segments results in a considerable loss of data and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG also carries brain signal (Peters, 1967, Oster & Stern, 1980), so regressing out eye artifacts inevitably involves subtracting a portion of the relevant EEG signal from each record as well. This method cannot be used for muscle noise or line noise for which there is no reference channel for regression. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records. The method is based on an extended version of an Independent Component Analysis (ICA) algorithm (Bell & Sejnowski, 1995; Lee & Sejnowski, 1997) for performing blind source separation on linear mixtures of independent source signals that can be sub- or super-Gaussian. Our results show that ICA can effectively detect, separate and remove the activity of a wide variety of artifactual sources in EEG records, with results comparing favorably to those obtained using regression methods.

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Removing Electroencephalographic Artifacts : Comparison between ICA and PCA


Tzyy-Ping Jung, Colin Humphries, Te-Won Lee, Scott Makeig, Martin J. McKeown, Vicente Iragui, Terrence J. Sejnowski. In: Neural Networks for Signal Processing VIII, 63-72, 1998.

Abstract

Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of an Independent Component Analysis (ICA) algorithm (Bell & Sejnowski, 1995; Lee & Sejnowski, 1997) for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can effectively separate and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably to those obtained using Principal Component Analysis.

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Analysis of fMRI by Blind Separation into Independent Spatial Components

Martin J. McKeown, Scott Makeig, Greg G. Brown, Tzyy-Ping Jung, Kindermann, and Terrence J. Sejnowski
Human Brain Mapping, 6(3):160-88, 1998.

Abstract

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

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Analyzing and Visualizing Single-trial Event-related Potentials

Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, and Sejnowski TJ
Advances in Neural Information Processing Systems 11, 118-24, 1999.

Abstract

Event-related potentials (ERPs), are portions of electroencephalographic (EEG) recordings that are both time- and phase-locked to experimental events. ERPs are usually averaged to increase their signal/noise ratio relative to non-phase locked EEG activity, regardless of the fact that response activity in single epochs may vary widely in time course and scalp distribution. This study applies a linear decomposition tool, Independent Component Analysis (ICA) (Lee et al., 1999), to multichannel single-trial EEG records to derive spatial filters that decompose single-trial EEG epochs into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain networks. Our results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event related background EEG activities into separate components, allowing (1) removal of pervasive artifacts of all types from single-trial EEG records, and (2) identification of both stimulus- and response-locked EEG components. Second, this study proposes a new visualization tool, the 'ERP image', for investigating variability in latencies and amplitudes of event-evoked responses in spontaneous EEG or MEG records. We show that sorting single-trial ERP epochs in order of a relevant response measure (e.g. reaction time) and plotting the potentials in 2-D clearly reveals underlying patterns of response variability linked to performance. These analysis and visualization tools appear broadly applicable to electrophyiological research on both normal and clinical populations.

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Functionally independent components of early event-related potentials in a visual spatial attention task

Scott Makeig, Marissa Westerfield, Jeanne Townsend, Tzyy-Ping Jung, Eric Courchesne
Philosophical Transactions: Biological Sciences, 354: 1135-44, 1999.

Abstract

Spatial visual attention modulates the first negative-going deflection in the human averaged event-related potential (ERP) in response to visual target and non-target stimuli (the N1 complex). Here we demonstrate a decomposition of N1 into functionally independent subcomponents with functionally distinct relations to task and stimulus conditions. ERPs were collected from 20 subjects in response to visual target and non-target stimuli presented at five attended and non-attended screen locations. Independent component analysis, a new method for blind source separation, was trained simultaneously on 500 ms grand average responses from all 25 stimulus attention conditions and decomposed the non-target N1 complexes into five spatially fixed, temporally independent and physiologically plausible components. Activity of an early, laterally symmetrical component pair (N1aR and N1aL) was evoked by the left and right visual field stimuli, respectively. Component N1aR peaked ca. 9 ms earlier than N1aL. Central stimuli evoked both components with the same peak latency difference, producing a bilateral scalp distribution. The amplitudes of these components were not reliably augmented by spatial attention. Stimuli in the right visual field evoked activity in a spatio-temporally overlapping bilateral component (N1b) that peaked at ca. 180 ms and was strongly enhanced by attention. Stimuli presented at unattended locations evoked a fourth component (P2a) peaking near 240 ms. A fifth component (P3f) was evoked only by targets presented in either visual field. The distinct response patterns of these components across the array of stimulus and attention conditions suggest that they reflect activity in functionally independent brain systems involved in processing attended and unattended visuospatial events.

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Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention

Scott Makeig, Marissa Westerfield, Tzyy-Ping Jung, James Covington, Jeanne Townsend, Terrence J. Sejnowski, and Eric Courchesne
Journal of Neuroscience, April 1, 1999, 19(7):2665-2680

Abstract

Human event-related potentials (ERPs) were recorded from 10 subjects presented with visual target and nontarget stimuli at five screen locations and responding to targets presented at one of the locations. The late positive response complexes of 25-75 ERP average waveforms from the two task conditions were simultaneously analyzed with Independent Component Analysis, a new computational method for blindly separating linearly mixed signals. Three spatially fixed, temporally independent, behaviorally relevant, and physiologically plausible components were identified without reference to peaks in single-channel waveforms. A novel frontoparietal component (P3f) began at ~140 msec and peaked, in faster responders, at the onset of the motor command. The scalp distribution of P3f appeared consistent with brain regions activated during spatial orienting in functional imaging experiments. A longer-latency large component (P3b), positive over parietal cortex, was followed by a postmotor potential (Pmp) component that peaked 200 msec after the button press and reversed polarity near the central sulcus. A fourth component associated with a left frontocentral nontarget positivity (Pnt) was evoked primarily by target-like distractors presented in the attended location. When no distractors were presented, responses of five faster-responding subjects contained largest P3f and smallest Pmp components; when distractors were included, a Pmp component appeared only in responses of the five slower-responding subjects. Direct relationships between component amplitudes, latencies, and behavioral responses, plus similarities between component scalp distributions and regional activations reported in functional brain imaging experiments suggest that P3f, Pmp, and Pnt measure the time course and strength of functionally distinct brain processes. Key words: electroencephalogram; event-related potential; evoked response; independent component analysis; reaction time; P300; motor; inhibition; frontoparietal; orienting

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Moving-Window ICA Decomposition of EEG Data RevealsS Evenet-Related Changes in Oscillatory Brain Activity


Scott Makeig*, Sigurd Enghoff (3,4). Tzyy-Ping Jung (3,5,6) and Terrence J. Sejnowskiung,
The 2nd Int'l Workshop on Indeppendent Component Analysis and Signal Separationical.

Abstract

Decomposition of temporally overlapping sub-epochs from 3-s electroencephalographic (EEG) epochs time locked to the presentation of visual target stimuli in a selective attention task produced many more components with common scalp maps before stimulus delivery than after it. In particular, this was the case for components accounting for posterior alpha and central mu rhythms. Moving-window ICA decomposition thus appears to be a useful technique for evaluating changes in the independence of activity in different brain regions, i.e. event-related changes in brain dynamic modularity. However, common component clusters found by moving-window ICA decomposition strongly resembled those found by decomposition of the whole EEG epochs, suggesting that such whole epoch decomposition reveals stable independent components of EEG signals.

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Independent Component Analysis of Biomedical Signals


Jung T-P, Makeig S, Lee T-W, McKeown M.J., Brown G., Bell, A.J. and Sejnowski TJ,
The 2nd Int'l Workshop on Indeppendent Component Analysis and Signal Separationical.

Abstract

Biomedical signals from many sources including hearts, brains and endocrine systems pose a challenge to researchers who may have to separate weak signals arriving from multiple sources contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment. The applications of Independent Component Analysis (ICA) to biomedical signals is a rapidly expanding area of research and many groups are now actively engaged in exploring the potential of blind signal separation and signal deconvolution for revealing new information about the brain and body. In this review, we survey some recent applications of ICA to a variety of electrical, magnetic and hemodynamic measurements, drawing primarily from our own research.

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Removing Electroencephalographic Artifacts by Blind Source Separation

Tzyy-Ping Jung, Scott Makeig, Colin Humphries, Te-Won Lee, Martin J. McKeown, Vicente Iragui, Terrence J. Sejnowski
Psychophysiology, 37: 163-78, 2000.

Abstract

Eye movements, eye blinks, cardiac signals, muscle noise and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally (Peters, 1967; Oster and Stern, 1980), regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of Principal Component Analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by Independent Component Analysis (ICA) (Lee et al., 1997; Bell and Sejnowski, 1995a). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably to those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

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Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, Terrence J. Sejnowski
Clinical Neurophysiology, 111(10): 1745-58, 2000.

Abstract

Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently. Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by Independent Component Analysis (ICA) (Bell and Sejnowski, 1995; Girolami et al., 1998; Lee et al., 1999) is presented here that overcomes these limitations. Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from EEG recordings. The results compare favorably to those obtained using rejection or regression methods. The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free.

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Event-related brain response abnormalities in autism: evidence for impaired cerebello-frontal spatial attention networks

Jeanne Townsend, Marissa Westerfield, Echo Leaver, Scott Makeig, Tzyy-Ping Jung, Karen Pierce, and Eric Courchesne
Cognitive Brain Research, 11(1): 127-45, 2001.

Abstract

Although under some conditions the attention-related late positive event-related potential (ERP) response (LPC) is apparently normal in autism during visual processing, the LPC elicited by visuospatial processing may be compromised. Results from this study provide evidence for abnormalities in autism in two components of the LPC generated during spatial processing. The early frontal distribution of the LPC which may reflect attention orienting was delayed or missing in autistic subjects during conditions in which attention was to peripheral visual fields. The later parietal distribution of the LPC which may be associated with context updating was smaller in amplitude in autistic subjects regardless of attention location. Both abnormalities suggest disruption of function in spatial attention networks in autism. Evidence that the cerebellar abnormalities in autism may underlie these deficits comes from: (1) similar results in ERP responses and spatial attention deficits in patients with cerebellar lesions; (2) brain?behavior correlations in normally functioning individuals associating the size of the posterior cerebellar vermis and the latency of the frontal LPC; and (3) a previously reported complementary correlation between the size of the posterior vermal lobules and spatial orienting speed. Although the scalp-recorded LPC is thought to be cortically generated, it may be modulated by subcortical neural activity. The cerebellum may serve as a modulating influence by affecting the task-related antecedent attentional process. The electrophysiological abnormalities reported here index spatial attention deficits in autism that may reflect cerebellar influence on both frontal and parietal spatial attention function.

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Imaging Brain Dynamics Using Independent Component Analysis

Tzyy-Ping Jung, Scott Makeig, Martin J. McKeown, Anthony Bell, Te-Won Lee, and Terrence J. Sejnowski,
Proceedings of the IEEE, 89(7):1107-22, 2001.

Abstract

The analysis of electroencephalographic (EEG) and magenetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent Component Analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this review, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.

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Analysis and visualization of single-trial event-related potentials

Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, Terrence J. Sejnowski
Human Brain Mapping, 14(3):166-85, 2001.

Abstract

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the ERP image, that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Hum. Brain Mapping 14:166-185, 2001. (C) 2001 Wiley-Liss, Inc.

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Dynamic brain sources of visual evoked responses.

Makeig S, Westerfield M, Jung T-P, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ.
Science, 295:690-4, Jan. 25, 2002.

Abstract

It has been long debated whether averaged electrical responses recorded from the scalp result from stimulus-evoked brain events or stimulus-induced changes in ongoing brain dynamics. In a human visual selective attention task, we show that nontarget event-related potentials were mainly generated by partial stim- ulus-induced phase resetting of multiple electroencephalographic processes. Independent component analysis applied to the single-trial data identiŽed at least eight classes of contributing components, including those producing cen- tral and lateral posterior alpha, left and right mu, and frontal midline theta rhythms. Scalp topographies of these components were consistent with their generation in compact cortical domains.

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Single-trial Variability in event-related BOLD signals

Jeng-Ren Duann, Tzyy-Ping Jung, Wen-Jui Kuo, Tzu-Chen Yeh, Scott Makeig, Jen-Chuen Hsieh, Terrence J. Sejnowski
NeuroImage, 15:823-35, 2002.

Abstract

Most current analysis methods for fMRI data assume priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events areas of interest. In addition, they typically homogeneity of both the HR and the non-HR signals, both across brain regions and across experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. Here, we use Infomax independent component analysis (ICA) to detect and visualize varia-tions in single-trial HRs in event-related fMRI data. subjects participated in four fMRI sessions which ten bursts of 8-Hz flickering-checkerboard stimu-lation were presented for 0.5-s (short) or 3-s (long) dura-tions at 30-s intervals. Five axial slices were acquired Bruker 3-T magnetic resonance imager at interscan intervals of 500 ms (TR). ICA decomposition of re-sulting blood oxygenation level-dependent (BOLD) from each session produced an independent component active in primary visual cortex (V1) and, in several ses-sions, another active in medial temporal cortex Visualizing sets of BOLD response epochs with BOLD-image plots demonstrated that component varied substantially and often systematically across tri-als as well as across sessions, subjects, and brain Contrary to expectation, in four of the six subjects component HR contained two positive peaks in response short-stimulus bursts, while components with identical regions of activity in long-stimulus from the same subjects were associated with single-peaked HRs. Thus, ICA combined with BOLD-image vi-sualization can reveal dramatic and unforeseen vari-ations not apparent to researchers analyzing their with event-related response averaging and fixed templates.

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4/05/01 - Tzyy-Ping Jung / CNL /The Salk Institute / jung@salk.edu