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Brain Electrophysiological Signal Processing: Postprocessing Paradigms

Explore postprocessing strategies in neural signal processing, including rhythmicity analysis, synchronization measures, and more. Learn about cortical rhythms and EEG oscillations.

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Brain Electrophysiological Signal Processing: Postprocessing Paradigms

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  1. ME (Signal Processing), IISc: Neural Signal Processing, Spring 2014 Brain Electrophysiological Signal Processing: Postprocessing Kaushik Majumdar Indian Statistical Institute Bangalore Center kmajumdar@isibang.ac.in

  2. Two Paradigms of Any Signal Processing Task • Preprocessing (cleaning) • Postprocessing (pattern recognition) ME (Signal Processing), IISc: Neural Signal Processing

  3. Postprocessing Paradigms • Rhythmicity Analysis • Synchronization Measure • Source Localization (for scalp EEG only) ME (Signal Processing), IISc: Neural Signal Processing

  4. Brain Oscillations http://www.addcentre.com/Pages/professionaltraining.html Projection of cortex on a two dimensional plane (XY), where neuronal firing rate is along the Z axis. ME (Signal Processing), IISc: Neural Signal Processing

  5. Facts about Cortical Rhythms • Mammalian forebrain can generate oscillations from 0.5 to 500 Hz. • EEG, ECoG and LFP follow “P varies as 1/f” law, where P is power and f is the frequency. • Prominent cortical rhythm frequencies are delta (0.5 – 4 Hz), theta (4 – 8 Hz), alpha (8 – 12 Hz), beta (12 – 30 Hz) and gamma (30 – 80 Hz). ME (Signal Processing), IISc: Neural Signal Processing

  6. Thalamus and Cortical Rhythm Generation Olejniczak, 2006 ME (Signal Processing), IISc: Neural Signal Processing http://en.wikipedia.org/wiki/Thalamus

  7. Universal EEG Oscillation Patterns in Sleep http://en.wikipedia.org/wiki/K-complex ME (Signal Processing), IISc: Neural Signal Processing

  8. Synchronous and Asynchronous Oscillations Pfurtscheller and Lopez de Silva, 1999 First band-pass filter the EEG signal. Then measure the power spectrum. High power spectrum indicates more synchronous activity in that region within that band width, for example mu-rhythm (10 – 12 Hz) in Rholandic fissure in (b) associated with movement. ME (Signal Processing), IISc: Neural Signal Processing

  9. Event Related Potential (ERP) P300 or P3 ERP. ERP = Specific brain electric potential waveform in response to a stimulus after a specified time lag. Notion of ERP is also extended to brain signals other than electric potentials. ME (Signal Processing), IISc: Neural Signal Processing http://cnecs.egr.uh.edu/brain-wave-analysis

  10. Nomenclature In order to measure ERP one needs to measure (1) amplitude and (2) latency of the ERP wave form. ME (Signal Processing), IISc: Neural Signal Processing http://en.wikipedia.org/wiki/Event-related_potential

  11. Measuring ERP (cont) • There are two common ways to measure ERP amplitudes. The most common method is to fix a time window and, for each waveform being measured, find the maximum amplitude in that time window. This is called peak amplitude measure (Luck, 2005). ME (Signal Processing), IISc: Neural Signal Processing

  12. Measuring ERP (cont) • Instead of the maximum amplitude when the average amplitude of a waveform in the window is measured it is called mean amplitude measure, which is the second most common way to measure ERP amplitude (Luck, 2005). ME (Signal Processing), IISc: Neural Signal Processing

  13. B D F A C E Measuring ERP: Peak Identification Majumdar et al., Brain Topography, 27: 112 – 122, 2014 ME (Signal Processing), IISc: Neural Signal Processing

  14. Peak Latency • Filter out the high-frequency noise in the EEG. • Rather than taking the maximum peak alone, take other local peaks also (possibly with some threshold), because the maximum peak may not always be due to an ERP waveform. • When different waveforms are compared they must have similar noise level. ME (Signal Processing), IISc: Neural Signal Processing

  15. Fractional Area Latency ME (Signal Processing), IISc: Neural Signal Processing

  16. References • G. Buzsaki and A. Draguhn, Neuronal oscillations in cortical networks, Science, 304: 1926 – 1929, 2004. • X.-J. Wang, Neurophysiological and computational principles of cortical rhythms in cognition, Physiological Rev., 90(3): 1195 – 1268, 2010. ME (Signal Processing), IISc: Neural Signal Processing

  17. References (cont.) • M. L. Van Quyen and A. Bragin, Analysis of dynamic brain oscillations: methodological advances, Trnds. Cog. Neurosci., 30(7): 365 – 373, 2007. • S. J. Luck, An introduction to the event related potential technique, 2e, MIT Press, 2005. ME (Signal Processing), IISc: Neural Signal Processing

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