[Eeglablist] Questions regarding preprocessing

[Kathrin Sadus]

Dear all,
As a PhD student, I have only recently started to learn about the processing and analysis of EEG data. As a novice, I have some questions regarding my preprocessing pipeline and hope to get some advice.  * Filtering As I am running an ICA, I use a 1 Hz high pass filter and transfer the resulting IC-weights on a 0.1Hz high pass filtered data set. Regarding the filtering, I was wondering which filter to implement. Based on my research I found that many people apply an IIR Butterworth filter. However, literature suggests that FIR filter are superior despite the higher computational costs. When applying a FIR Filter (let’s say the eeglab default Hemming filter), I would need a long filter, as my cut off is quite low (0.05Hz). I wanted to apply the filter to my continuous data set to avoid edge artifacts, which was recorded with a 1000 Hz sampling rate. Given that I am applying the filter to my continuous data set which has over 8.000.000 data points before resampling, would it be fine to use a FIR Filter? Using the default values, I ended up with a 33001-point high pass filtering. Based on my sampling rate my filter would have impulse response duration of 33 seconds and a transition band width if 0.1, am I correct? Assuming that I am not including the first 33 seconds in my data analysis would it be fine to use such a FIR Filter or are there any other drawbacks of a long impulse response duration? I checked the channel data to get a feeling for the impact of the different filters (butterworth, hemming), but the data looked exactly the same at first glance (which is essentially a good thing). However, I was wondering whether there are any suggestions regarding transition band width, since I simply used the default values to become more familiar with the filter.
  * Averaging During the acquisition, we used a Cz online reference. First, I used an average offline reference but I was worried that the channel density is too low with 32 electrodes resulting in biased averaging. Thus, I decided to perform a linked mastoid reference. However, I was wondering whether it is necessary to reconstruct the Cz in my dataset before performing an average reference to avoid rank deficiency. When using the average reference, I have simply adjusted the data dimension within the ICA by using 'pca' and specifying nchan-1. However, I was wondering whether reconstructing the online reference would be a better way to deal with the data dimensions.I apologize in advance if my understanding is deficient at some points and I appreciate your advice.

 Best,

Kathrin
 
 
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Heidelberg Universty - Institue of Psychology

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