Single-trial ERPs

Extracting single-trial evoked responses from spontaneous EEG

Tzyy-Ping Jung, Scott Makeig, Terrence J. Sejnowski.

Introduction

Event-Related Potential (ERP) averages of electrical responses to sensory stimuli recorded at the human scalp capture voltage fluctuations both time locked and phase locked to occurrence of the stimuli. It is widely suspected, though poorly documented, that in single stimulus epochs the response activity may vary widely in both time course and scalp distribution. The major difficulty in comparing single trials is that the spontaneous EEG activity may obscure response-evoked activity, since spontaneous EEG is typically much larger than the evoked response. Independent Component Analysis (ICA) constructs spatial filters that can separate ERPs into spatially-fixed, temporally-sparse components that are temporally independent of one another.

References

  1. Makeig S, Jung T-P, and Sejnowski TJ, "Independent Component Analysis of Single-trial Event-related Potentials", Society for Neuroscience Abstracts, Oct. 1997.
  2. 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.
  3. Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, and Sejnowski TJ, "Analyzing and Visualizing Single-trial Event-related Potentials," In: Advances in Neural Information Processing Systems 11:118-24, 1999.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Makeig S, Delorme A, Westerfield M, Townsend J, Courchense E, Sejnowski T, Electroencephalographic brain dynamics following visual targets requiring manual responses. PLOS Biology 2(6):747-62, 2004.
  13. Huang, R-S. Jung, T-P and Makeig, S. Analyzing Event-Related Brain Dynamics in Continuous Compensatory Tracking Tasks , Proc of the 27th Int'l Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, 2005.
  14. Tasi, A.C., Liou, M., Jung, T-P., Onton, J., Cheng, P., Wang, H-C., Duann, J.R., Makeig, S. Mapping ERPs on the Cortical Surface through a Spatiotemporal Modality, Neuroimage, 23(1): 195-207, 2006.

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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10/16/2007 - Tzyy-Ping Jung / Swartz Center for Computational Neuroscience/ UCSD / jung@sccn.ucsd.edu