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 kmajumdar@isibang.ac.in ME (Signal Processing), IISc: Neural Signal Processing

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

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

ME (Signal Processing), IISc: Neural Signal Processing 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

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

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

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

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

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

ME (Signal Processing), IISc: Neural Signal Processing 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

ME (Signal Processing), IISc: Neural Signal Processing 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

ME (Signal Processing), IISc: Neural Signal Processing 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

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

ME (Signal Processing), IISc: Neural Signal Processing 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

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

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

ME (Signal Processing), IISc: Neural Signal Processing 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