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

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. ME (Signal Processing), IISc: Neural Signal Processing
Brain Oscillations
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

7. Universal EEG Oscillation Patterns in Sleep
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

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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

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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

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

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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