Analysis Pipeline

Hi Mike,

It’s guiding to see your lectures and implement them. I just want an expert comment on my pipeline. Your comment might mark some subtle or major conceptual errors. The pipeline is as follows:

  1. Event Extraction (Single Trial)

  2. Bandpass filtering 4.0-40.0 Hz

  3. Re-referencing (Average)

  4. Correlation and thresholding based bad channel detection and interpolation

  5. ICA computation, Automatic Component Labeling, and Rejection of components with artifacts namely eye, muscle, heart, channel noise, and line noise.

  6. Baseline normalization (Decibel Conversion) to counter the gradual change in impedance (EGI EEG System with saline solution), subject variation, 1/f phenomena (though it has been taken care of by filtering itself), approx normal distribution of normalized data.

Please comment on this.

Hi Asif. Is this the same question you posted on one of my youtube videos? A few comments:

  • You should filter before epoching. Thus, filter the continuous data.
  • 4 Hz seems a bit high, although it depends on what analyses you’re planning on running. Most people set the high-pass filter to be .1 Hz or .5 Hz.
  • It’s best to remove bad channels before computing the average reference. Otherwise, the artifacts from those channels will project onto all the other channels.
  • Many people know that I do not recommend automatic IC rejection, particularly not for so many data features. I don’t even think ICA can cleanly separate those sources of noise. I would pick the ICs by careful inspection and only remove the eye-related components. (In fairness, ICA-based cleaning is more opinion-based than fact-based, so you should treat this as a suggestion not a mandate.)

More generally, I and many other people have written and published a lot about EEG data cleaning and pre-processing.