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Workshop Program

Mining Cognitive Brain Dynamics I

Online EEGLAB Workshop -- University of California San Diego (UCSD), La Jolla, CA - November 18, 2010

This Talk -- Scott Makeig, Director of the Swartz Center for Computational Neuroscience (SCCN), UCSD, and founder / co-developer of the EEGLAB project, begins the Workshop with a (November 2010) overview of EEG research, its motivation, biology, analysis, and potential future applications, and an overview of the original EEG data processing approach that EEGLAB enables.

You may download the (.pdf) slides used in the talk here. Press FS on the lower right corner of the video image to view the talk in full screen display.

Chapter 1 (8:08) gives a brief history of the development of the EEGLAB environment project, including its origin in the ICA Electrophysiological Data Analysis Toolbox first put online by Makeig and colleagues at Salk Institute in the laboratory of Terry Sejnowski at Salk Institute in 1997. Download the slides for this chapter.

Terms introduced (with links):

  • Swartz Center for Computational Neuroscience (SCCN)
  • Electroencephalogram (EEG)
  • Event-Related Sectral Perturbation (ERSP)
  • Independent Component Analysis (ICA)
  • Trial-by-trial response plotting (what is it?)
  • SALK Institute
  • Graphic-User Interface (GUI)
  • National Institutes of Health (US) - (NIH)
  • EEGLAB (plugins)
  • STUDY structure
  • Neuroelectromagnetic Forward head-modeling Toolbox (NFT)
  • Data River
  • Experimental Recording and Interactive Control and Analysis environment (ERICA)
  • Human Electrophysiology, Anatomic Data, and Integrated Tools resource (Head IT)
  • EEGLAB visitor widget (at page bottom)

Study the slides for this chapter here.

    For further reading:

Chapter 2 (6:59) places EEG analysis in its most basic context, the age old question and "heart of the matter" -- 'Who Am I?.' It discusses the growing importance to cognitive neuroscience of observing and modeling patterns of distributed brain dynamics supporting our experience and behavior, a quest in which non-invasive high-density EEG imaging appears destined to play an important role. Download the slides for this chapter.

  • What is the brain trying to optimize?

  • What kinds of challenges do brains have to meet?

  • What may the brain be doing beyond the 'perception-evaluation-action' cycle?

Study the slides for this chapter here.

For further reading:

Chapter 3 (15:02) discusses the central biological question on which any cogent EEG research program must be built, 'What is EEG?'. The history of functional brain imaging (beginning with the development of EEG recording about 1926) is reviewed. The difficult problem that brain dynamics are deeply multiscale is emphasized. Download the slides for this chapter.

  • In what ways are brain dynamics "inherently multi-scale"?

  • How might this fact best be observed and modeled?

  • What is "spatial scale chauvinism"?

  • How can cross-scale coupling be bi-drectional?

  • What are cortical phase cones? Are these the same as cortical avalanches?

  • How are phase cones related to far-field potentials ?

  • What are microscopic, mesoscopic (Freeman), and macroscopic brain dynamics?

  • How does EEG image local cortical synchrony?

Study the slides for this chapter here.

   For further reading:

Chapter 4 (7:56) discusses the marked difference between scalp EEG data channel signals themselves and the underlying cortical EEG source processes. The very broad 'point-spread' function characterizing the spread of potential from cortex to scalp is a basic obstacle to using EEG to perform 3-D functional brain imaging -- one that spatial source filtering methods developed in the last 20 years can powerfully alleviate. Download the slides for this chapter (pdf).

  • How and why are scalp electrode signals 'mixtures' of source signals?

  • What is the problem with considering scalp EEG signals to be discrete 'point processes' rather than mixtures of source signals?

  • Why is the mixing process linear?

  • How are scalp potentials determined by phase coherence within cortical patches?

  • How does the width of a cortical source patch affect its degree of phase synchrony?

  • How does the 'source mixture' concept contrast with the view that EEG signals are 'noisy'?

Study the slides for this chapter here.

   For further reading:

Continue to Part 2 of the talk