General Information on the MATLAB ICA Toolbox for Electrophysiological Data

==> A major toolbox update, "EEGLAB" (v4.0) is now available here
Previous ICA/EEG toolbox version: 3.75 (release of 9/03/01)

Written for Matlab Unix versions: >5.0. Version 4.2c no longer supported.
Version 5 code is also compatible with Version 6.

Run demonstration: >> icademo % But first replace the string 'XXX' in icadefs.m
List of functions:     >> help ica
Browse the Tutorial: >> tutorial

List of publications on ICA applied to EEG and fMRI data.

Frequently-asked questions about ICA (icafaq.html).

Browse the Online Web Tutorial

Version History:

1.0 11-30-96 First release, CNL / Salk Institute (Makeig et al.)
Other authors: Jung, Humphreys, Enghoff, Westerfield, etc.
3.75 9-3-01 Latest release

The ICA/EEG toolbox has been downloaded over 5000 times by users from over 50 countries and research fields. (To see a partial listing of their intended applications, click here). Please email feedback about things that do or don't work, or any suggested improvements. If you improve your working versions of the scripts, please send me a copy or description. I will credit your suggestions.

- Scott Makeig

Functions/features/fixes in v3.75:

An ONLINE EEGLAB WORKSHOP and Detailed tutorial wiki on EEGLAB are now available.

--> The tutorial has now been carefully checked and debugged, so you should be able to follow it by cutting and pasting its sample code into a Matlab window. You may also download the tutorial and install it locally in your matlab directory. We are continually enhancing it; your comments and suggestions welcome.

NEW FUNCTIONS in v3.7 ** compdsp() Display components of a data decomposition: Four windows show
comp. amplitudes, scalp maps, activations, and activation spectra.
** caliper() Use an ICA (or PCA, etc) component as "calipers" to measure
your data. Use to test grand mean ERP components across
single subjects or conditions.
** gradplot() Return and/or plot gradient of given scalp map(s)
** lapplot() Return and/or plot laplacian of given scalp map(s)
** readlocs() Read 2-D and 3-D channel location files
** spectopo() Plot the mean spectrum of each channel of an input data matrix
plus topoplot()s of relative power at selected frequencies
** tftopo() Plot an ERSP or ITC image for a given data channel (from timef())
plus topoplot()s of all-channel values at selected time/freq points
** tutorial() The tutorial code has been checked for accuracy.
Type >> tutorial to browse the Toolbox tutorial on your web
browser! If you download the tutorial to your local disk
(see the toolbox download page), tutorial() will browse
your local tutorial pages, avoiding network delays.

NEW FEATURES/fixes in v3.75

compdisp() - Added intact code for this new function (see above).
erpimage() - Fixed some layout and arg reading errors in erpimage(). Fixed
inputing of stored baseamp and signifs args. Allowed >> erpimage(data)
==> Added 'auxvar' plotting of auxiliary variables (n per trial).
sbplot() - Made sbplot() tile a given axis handle (e.g., gca -> recursive!).
compmap() - Used this to make compmap() plot maps in the gca.
icaproj() - Updated order of icaproj() args; adjusted functions that use this.
envproj(), plotproj(), icaproj() - Removed 'sphere' argument (incorporated it
into the 'weights' arg).
rmbase() - Added trap for length(basevector) == 1, added [] defaults BUGS: ** In erpimage(), the 'phase' option may not work in MATLAB 5.2 (but ok in 5.3)?? ** In erpimage(), saving plots as -depsc or -djpeg may print faint vertical axis in the coher or erp axis (NB: Not seen on screen --> a Matlab bug!?).

NEWS of ICA applications to biomedical research
(New/additional references welcome)

Some of MANY RECENT PUBLICATIONS using ICA decomposition of biomedical data. Publications using ICA are increasing rapidly. For example, this year the Human Brain Mapping meeting in Brighton UK (June) included well over 20 abstracts on ICA and fMRI/EEG/MEG (last year: 10).

  • =========================REVIEW=========================
  • Jung, T-P, Makeig, s, McKeown, M. J., Bell, A. J., Lee, T-W, Sejnowski, T. J., Imaging brain dynamics using Independent Component Analysis (.pdf, 640k), Proceedings of the IEEE, 89(7):1107-22, 2001.
  • =========================2001=========================
  • Calhoun VD, Adali T, Pearlson GD and Pekar JJ, "Spatial and temporal independent component analsysis of functional MRI data containing a pair of task-related waveforms. Human Brain Mapping 13:1:43-53, 2001.
  • Callan DE, Callan AM, Kroos C, Vatikiotis-Bateson E. Multimodal contribution to speech perception revealed by independent component analysis: a single-sweep EEG case study. Brain Res Cogn Brain Res. 10(3):349-53, 2001.
  • Ines Jenztch and W. Sommer, "Sequence-sensitive subcomponents of P300: Topographical analyses and dipole source localization." Psychophysiology 38:607-621, 2001.
  • T-P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne and T. J. Sejnowski, "Independent component analysis of single-trial event-related potentials," Human Brain Mapping, 14(3):168-85,2001.
  • Kobayashi K, Merlet I, Gotman J. Separation of spikes from background by independent component analysis with dipole modeling and comparison to intracranial recording. Clin Neurophysiol. 112(3):405-413, 2001.
  • Townsend, J., Westerfield, M., Leaver, E., Makeig, S., Jung, T-P., Pierce, K. & Courchesne, E. Abnormalities of topography and composition of ERP responses in autism during spatial attention. Cognitive Brain Research 11:127-145, 2001.
  • Ying Zheng, David Johnston, Jason Berwick, John Mayhew, Signal Source Separation in the Analysis of Neural Activity in Brain, Neuroimage 13:447-458, 2001.
  • =========================2000=========================
  • Ikeda S, Toyama K. Independent component analysis for noisy data--MEG data analysis. Neural Netw. 13(10):1063-74, 2000.
  • Jung, T-P., Humphries, C., Lee, T-W., McKeown, M. J., Iragui, V., Makeig, S. and Sejnowski, T. J., "Removing electroencephalographic artifacts by blind source separation," Psychophysiology 37:163-178, 2000.
  • Knief A, Schulte M, Bertrand O, Pantev C. The perception of coherent and non-coherent auditory objects: a signature in gamma frequency band. Hear Res. 145(1-2):161-8, 2000.
  • Laubach, M., Wessberg, J. and Nicolelis, M.A.L. Cortical ensemble activity increasingly predicts behavioral outcomes during learning of a motor task. Nature, 405:567-571, 2000.
  • McKeown MJ. Cortical activation related to arm-movement combinations. Muscle Nerve. Suppl 9:S19-25. 2000.
  • Vigario R, Oja E. Independence: a new criterion for the analysis of the electromagnetic fields in the global brain? Neural Netw. 13(8-9):891-907, 2000.
  • Vigario R, Sarela J, Jousmaki V, Hamalainen M, Oja E. Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans Biomed Eng. 47(5):589-93, 2000.
  • Wessberg, J., Stambaugh, C.R., Kralik, J.D., Beck, P.D., Laubach, M., Chapin, J.K., Kim, J., Biggs, S.J., Srinivasan, M.A., and Nicolelis, M.A.L. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature, 408:361-365, 2000.
  • Wubbeler G, Ziehe A, Mackert BM, Muller KR, Trahms L, Curio G. Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans. IEEE Trans Biomed Eng. 47(5):594-9, 2000.
  • =========================1999=========================
  • Kobayashi K, James CJ, Nakahori T, Akiyama T, Gotman J. Isolation of epileptiform discharges from unaveraged EEG by independent component analysis. Clin Neurophysiol. 110(10):1755-63, 1999.
  • Laubach, M., Shuler, M., and Nicolelis, M.A.L. Independent component analyses for quantifying neuronal ensemble interactions. Journal of Neuroscience Methods, 94:141-154, 1999.
  • McKeown MJ, Humphries C, Iragui V, Sejnowski TJ. Spatially fixed patterns account for the spike and wave features in absence seizures. Brain Topogr. 12(2):107-16, 1999.
  • Nicolelis, M.A.L., Ghazanfar, A.A., Stambaugh, C.R., Oliveira, L.M., Laubach, M., Chapin, J.K., Nelson, R.J., and Kaas, J.H. Simultaneous encoding of tactile information by three primate cortical areas. Nature Neuroscience, 1:621-630, 1998

For further information:

Scott Makeig home page
Tzyy-Ping Jung ICA page
Early ICA Bibliography

Scott Makeig
CNL / Salk Institute
December 15, 2000

P.S. While many of the tools in this toolbox are specialized for electrophysiological research, general interest in ICA is exploding.

Some of the ICA applications listed by users who have downloaded the ICA/EEG Matlab toolbox:

EEG Signal Processing; Nonlinear dynamics in EEG
Analysis of EEG using the chaos theory
EEG spatio-temporal analysis; EEG and cognitive processes
Inverse problems in EEG, MEG;
Sleep Research, EEG analysis, vigilance analysis;
Sleep spindles; EEG Microarousals in sleep;
Alertness monitoring
Wavelet analysis of EEG signals
Autonomic nervous system & EEG, GSR
Neurofeedback for children EEG
EEG data from AD and VD patients
MEG data on speech perception;
MEG and EEG; DC-Magnetometry
MEG, Neural control and neural plasticity
Auditory Evoked Potentials
Cognitive classification using ERPs
Visual VEP/EEG with depth perception.
Single trial evoked potential detection
Motor potentials; EMG
Eye movements
Classical conditioning
Auditory evoked response data from cochlear implant users.
Acoustic emissions
PET; PET stimulation data;
Functional brain imaging research
fMRI & MEG integration
Visual processing using fMRI & MEG
High Resolution ECG improvements using ICA
Cardiac electrograms ECG;
EEG/EKG data analysis and modelling
Epilepsy Research
Brain-computer interface BCI
Multi-electrode single-unit recordings in the CNS
Population vectors from multineuron recordings
Optical imaging of visual cortex;
Separating out vascular motion from optical maps
Multivariate hormonal dynamics
Protein bioinformatics
Nonstatonary astronomical time series
Speech recognition; Speech in noise;
Spectroscopic data
Sound spectrograms
Image processing and de-noising
CCD imaging and sound separation
Ultrasonic research
Spacecraft fault detection/prediction; machine vibration signals
In vivo and in vitro pharmacological applications
Telecommunications; Multichannel transmission
Control and adaptive systems
Pattern recognition and learning
Computational structural molecular biology
Applied mathematics, ill-posed problems
Nonlinear brain dynamics;
Motor control of Octopus
Brain transplants, somatosensory plasticity
New model of the synapses and endocrine factors
Cognitive Neuroscience - signal and image processing
Medical image analysis and pattern recognition
Image processing; Image Denoising
Hyperspectral visible and infrared data
Eddy Current Sensors
Neural Networks, Artificial Intelligence, Agent Based systems
Statistics; Konnektionismus; neural networks
Target tracking
Neuro-fuzzy methods applied to nonlinear systems
Human Engineering; Neuroengineering;
Finanical prediction; Data mining in finance;
The world of ODE
Ionospheric tomography
Ecological data analysis
Hyperspectral geology
Video understanding
Sperm dynamics
"Telepathy, etc."

Back to Top

Current EEGLAB Toolbox tutorial

Download the EEGLAB environmen softwaret