Imaging brain dynamics using independent component analysis

The analysis of electroencephalographic (EEG) and magnetoencephalographic (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 paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.

[1]  W. Ritter,et al.  The scalp topography of potentials in auditory and visual discrimination tasks. , 1977, Electroencephalography and clinical neurophysiology.

[2]  P. Nunez,et al.  Electric fields of the brain , 1981 .

[3]  M. Scherg,et al.  Evoked dipole source potentials of the human auditory cortex. , 1986, Electroencephalography and clinical neurophysiology.

[4]  Christian Jutten,et al.  Space or time adaptive signal processing by neural network models , 1987 .

[5]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[6]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[7]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[9]  S. Makeig.  Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. , 1993, Electroencephalography and clinical neurophysiology.

[10]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[11]  Andrzej Cichocki,et al.  Robust learning algorithm for blind separation of signals , 1994 .

[12]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[13]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[14]  Terrence J. Sejnowski,et al.  Edges are the Independent Components of Natural Scenes , 1996, NIPS.

[15]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[16]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[17]  T J Sejnowski,et al.  Learning the higher-order structure of a natural sound. , 1996, Network.

[18]  I. Kermadi,et al.  Effects of reversible inactivation of the supplementary motor area (SMA) on unimanual grasp and bimanual pull and grasp performance in monkeys. , 1997, Somatosensory & motor research.

[19]  S Makeig,et al.  Blind separation of auditory event-related brain responses into independent components. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[21]  Philippe Garat,et al.  Blind separation of mixture of independent sources through a quasi-maximum likelihood approach , 1997, IEEE Trans. Signal Process..

[22]  Martin J. McKeown,et al.  Removing electroencephalographic artifacts: comparison between ICA and PCA , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[23]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[24]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[25]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[26]  A. J. Bell,et al.  Response from Martin McKeown, Makeig, Brown, Jung, Kindermann, Bell and Sejnowski , 1998, Trends in Cognitive Sciences.

[27]  P. Achermann,et al.  A new method for detecting state changes in the EEG: exploratory application to sleep data , 1998, Journal of sleep research.

[28]  Tzyy-Ping Jung,et al.  Analyzing and Visualizing Single-Trial Event-Related Potentials , 1998, NIPS.

[29]  D. Cheyne,et al.  Dynamic cortical activity in the human brain reveals motor equivalence , 1998, Nature.

[30]  T. Sejnowski,et al.  Human Brain Mapping 6:368–372(1998) � Independent Component Analysis of fMRI Data: Examining the Assumptions , 2022 .

[31]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[32]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[33]  A K Liu,et al.  Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Klaus Prank,et al.  Independent Component Analysis of Intracellular Calcium Spike Data , 1998, NIPS.

[35]  Karl J. Friston.  Modes or models: a critique on independent component analysis for fMRI , 1998, Trends in Cognitive Sciences.

[36]  Mark A. Girolami,et al.  An Alternative Perspective on Adaptive Independent Component Analysis Algorithms , 1998, Neural Computation.

[37]  M. Laubach,et al.  Independent component analyses for quantifying neuronal ensemble interactions , 1999, Journal of Neuroscience Methods.

[38]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

[39]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[40]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[41]  J. Gotman,et al.  Isolation of epileptiform discharges from unaveraged EEG by independent component analysis , 1999, Clinical Neurophysiology.

[42]  S Makeig,et al.  Functionally independent components of early event-related potentials in a visual spatial attention task. , 1999, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[43]  T. Sejnowski,et al.  Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention , 1999, The Journal of Neuroscience.

[44]  Marissa Westerfield,et al.  INDEPENDENT COMPONENT ANALYSIS OF SINGLE-TRIAL EVENT-RELATED POTENTIALS , 1999 .

[45]  G. Berns,et al.  Continuous Functional Magnetic Resonance Imaging Reveals Dynamic Nonlinearities of “Dose-Response” Curves for Finger Opposition , 1999, The Journal of Neuroscience.

[46]  V. Haughton,et al.  Whole-brain functional MR imaging activation from a finger-tapping task examined with independent component analysis. , 2000, AJNR. American journal of neuroradiology.

[47]  A. J. Bell,et al.  INDEPENDENT COMPONENT ANALYSIS OF BIOMEDICAL SIGNALS , 2000 .

[48]  T. Sejnowski,et al.  Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects , 2000, Clinical Neurophysiology.

[49]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[50]  L. Zhukov,et al.  Independent component analysis for EEG source localization , 2000, IEEE Engineering in Medicine and Biology Magazine.

[51]  Shiro Ikeda,et al.  Independent component analysis for noisy data -- MEG data analysis , 2000, Neural Networks.

[52]  M. McKeown.  Detection of Consistently Task-Related Activations in fMRI Data with Hybrid Independent Component Analysis , 2000, NeuroImage.

[53]  Klaus Obermayer,et al.  Blind signal separation from optical imaging recordings with extended spatial decorrelation , 2000, IEEE Transactions on Biomedical Engineering.

[54]  M E Meyerand,et al.  Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets. , 2000, Magnetic resonance imaging.

[55]  M. Girolami,et al.  Advances in Independent Component Analysis , 2000, Perspectives in Neural Computing.

[56]  M. Laubach,et al.  Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task , 2022 .

[57]  T. Sejnowski,et al.  MOVING-WINDOW ICA DECOMPOSITION OF EEG DATA REVEALS EVENT-RELATED CHANGES IN OSCILLATORY BRAIN ACTIVITY , 2000 .

[58]  Allan Kardec Barros,et al.  Extraction of event-related signals from multichannel bioelectrical measurements , 2000, IEEE Trans. Biomed. Eng..

[59]  Terrence J. Sejnowski,et al.  Independent Component Analysis of Simulated ERP Data , 2000 .

[60]  Barak A. Pearlmutter,et al.  Blind source separation of multichannel neuromagnetic responses , 2000, Neurocomputing.

[61]  Erkki Oja,et al.  Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.

[62]  S Makeig,et al.  A natural basis for efficient brain-actuated control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[63]  T. Sejnowski,et al.  Independent component analysis at the neural cocktail party , 2001, Trends in Neurosciences.

[64]  E. Oja,et al.  Independent Component Analysis , 2013 .

[65]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.