[Eeglablist] (Too) long of an explanation and questions at the end

Tom Campbell tom_campbell75 at hotmail.com
Sun Feb 16 23:27:19 PST 2014

The most serious noise issue concerns the electrodes. So the electronics are rather good at putting lipstick on a pig! The device has built-in filters, though additional offline filtering is an option.  People use this device for BCI without offline filtering.  It is possible to customise with better electrodes:https://github.com/SmartphoneBrainScanner/smartphonebrainscanner2-hardware/blob/master/easycap/build_instructions.md

These may be of interest:
Debener, S., Minow, F., Emkes, R., Gandras,
K. & de Vos, M. How about taking a low-cost, small, and wireless eeg for a
walk?. Psychophysiology 49 1617–1621 doi:http://dx.doi.org/10.1111/j.1469-8986.2012.01471.x ( 2012)   


From: arno at salk.edu
Date: Fri, 14 Feb 2014 20:44:45 -0800
To: sharonjalene at gmail.com
CC: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] (Too) long of an explanation and questions at the	end

I epoch the data before statistical analyses even though I am looking at the
differences between the Grand Average – within subjects?
Yes, you need to extract data epochs to compute the grand average ERP. If you are computing the spectrum (based on the rest of your message), then epoch extraction is not necessary.2.   
I am HP filtering at 1Hz and using
Cleanline, automatic artifact rejection, visual artifact rejection.  I have played with ICA component rejection,
but am not sure I have enough good data to do so. (I recorded pilot data on one
subject – I have 3 minutes of data for each condition and 14 electrodes).   Is
there an optimal filtering method since I am using the EMOTIV?
This is a very noisy system. To extract meaningful ICA components, the more filtering the better.

One of my committee said I should NOT filter
the data or remove artifacts since I am just looking for the Grand Average of
certain freq bands.  Although I see his logic, I believe I should
still remove spurious data so that the mean is not skewed by muscle and other
artifacts… right?
Yes, if you have strong drifts in your data or DC shifts, you will need to filter. Otherwise, it will ruin the whole spectrum.

I was able to create a STUDY.  I saved separate data sets with the
electrodes of interest. I was able to run power spectrum channel stats as a 3
(conditions) X 2(Left and Right) ANOVA. 
I saw the t-tests between conditions and the interaction.   

Is the power spectrum giving me the average
of the specified freq range?

If I epoch the data, can I use Bootstrapping
instead of parametric or permutation?

No, this is independent.

How can I get the actual values, including
the p values, of the stats?  I see the
option to show a table with statistics containing the median, mean, mode, etc.,
but it rarely populates and is inconsistent. IS that statcond – on?
Look at the history and add output to the function that plot the STUDY spectrum (the function name is std_spec). This function will return the statistics (which are computed by statcond).

If I show results from a bootsrap, what
statistical test was used to obtain the results? 
The t-value (paired or unpaired) or the F-value is computed. The bootstrap procedure randomly samples the data and allow EEGLAB to obtain confidence intervals for these values under the null hypothesis.

Would I be better off using a ratio of Grand Average subtraction of the control from the FOCUS conditions. or pwelch in MATLAB  -then dropping the data into SPSS for some non parametric analysis (WIlcoxon rank ordered)?
Sure. You should try what you are comfortable with. The results should be similar. 
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