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Signal Processing: EEG to ERP

Signal Processing: EEG to ERP. A Darren Parker Presentation. EEG to ERP. To get an ERP signal out of the raw EEG, our goal must be to reduce the signal to noise ratio. How can we reduce noise?. Averaging trials in the same condition together.

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Signal Processing: EEG to ERP

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  1. Signal Processing: EEG to ERP A Darren Parker Presentation

  2. EEG to ERP To get an ERP signal out of the raw EEG, our goal must be to reduce the signal to noise ratio.

  3. How can we reduce noise? • Averaging trials in the same condition together. • Filter out frequencies that are not of any concern. (i.e. 60Hz noise) • Rereference data so channels become more uniformly emphasized with relation to the reference channel. • Reject “bad” trials. • Correct for artifacts in the data, like blinks.

  4. Filtering

  5. Filtering • Waveforms can be broken down into a series of sine waves at different frequencies. • Our filters separate the signal into the individual sine components, then remove the components we aren’t interested in.

  6. Rereferencing • Need a reference for each electrode to be measured relative to. • Electrodes near the reference will show little signal, since there is not a great deal of difference between them. • Electrodes far away will be overrepresented since they are far away and much more different.

  7. Rereferencing Cz Reference Rereferenced

  8. Rejecting Bad Trials • Sometimes parts of the waveform are just messy, due to subjects moving around in one way or another. • Some artifacts are common and stereotyped, such as blinks, and sometimes eye movements. • Potential for correction factors. • Others are more random with no predictable pattern.

  9. Rejection Blink Eye Movment Garbage

  10. Eye Blink Correction • Blinks and (sometimes) eye movements are common, and an individual’s blinks and eye movements may be fairly constant. • We can try to build a prototype for a subject, so we can correct for blinks (and eye movements).

  11. Averaging • The EEG records all changes in potentials at all electrodes, not just the signals we are interested in. There will always be additional noise since our subjects aren’t dead. • Assuming that other processes are random, and not time-locked to the stimulus, averaging trials together in any given condition will increase the signal and decrease any random noise.

  12. Averaging Both pictures taken from: http://neurocog.psy.tufts.edu/images/erp.htm

  13. The Plot Thickens • By now you should have your perfect little ERP averages… but is everything really that perfect? • Bad channels affect averaging and rereferencing. • Filtering is not perfect. • What reference is best? • Latency jitter. • Artifact correction woes.

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