Is there any way I can import EEG data pre-processed in Brain Vision Analyzer into FieldTrip, so that I don't have to reprocess the data in FieldTrip?

I am trying to export EEG data as ascii files with each trial as a series of rows. To clarify, I have 32 channels and 200 trials and I am trying to generate a data set of 6400 rows. I have used various export functions but I haven't been able to generate a file in this format (compatible for Matlab). Is this possible?

Hello,

I am using BrainVision Analyser to analyse EEG theta-activity in a reality-monitoring exclusion memory paradigm (two encoding conditions; imagine/perceive and three target designations at retrieval; target/non-target/new). I have used the Complex Morelet Wavelet function. I have attached print screens of my pre-processing steps for your reference.

I am struggling to export the values for statistical analysis. From reading the BrainVision manual, I understood this could be done via the Area Export function. I have attached a print screen of the options available in case I have missed something obvious. However this always produces the same error "no history domain with frequency domain found". I can get an export using the Generic Data Export function. This exports power for each participant at each time point for each condition separately. I can do this for each participant (44) and condition (4), but this will be time consuming; I wanted to check this is correct before I commit to it. Alternatively, if I have misunderstood something entirely, I would greatly appreciate your input.

Thank you for taking the time to read this. I look forward to hearing from you,

Amie

Does anyone could share with me the steps to analyze the ERP data using BrainVision Analyzer 2? My data was collected using Brain Products V-Amp 8 channels.

Thank you in advance.

I have 217 preprocessed BrainVision matlab files (.mat) with ECG data in them. I've been having trouble creating a study for these. I know BioSig works for ECG data, but it doesn't seem to work for BrainVision files.

" struct with fields:

message: 'Unrecognized field name "brainvision".'"

bva-io handles brainvision files but didn't work either, I believe because of the ECG data.

Any suggestions would be much appreciated.

Hi everyone and thanks for your attention.

I am analysing EEG data for a research project (I am using EEG- and ERP-Lab). Sometimes I see channels with a bad signal for only half of the trace. It seems a shame to waste so much good signal by interpolating the whole channel and I can't remove only some epochs.

I have recently started analysing EEG data, so, both theoretically and practically, I don't know if the issue can be solved by interpolating only half of the channel trace.

If you have already dealt with this issue, let me know what you think!

Thanks again,

Angelo

Dear all,

I recently tried to optimize my scripts to preprocess EEG data using EEGLAB2019. I found that including EOG channels in ICA significantly improved the subsequent ERP results, i.e., the classic LPP appeared. However, if I included EOG channels in ICA, the subsequent average re-reference had to also include them. If I performed average re-reference on only brain channels, the eeg_checkset would report an error ( ICA Index exceeds matrix dimensions). If I excluded EOG channels in ICA, average re-reference on only brain channels worked well, which, however, generated a bad ERP wave.

The figure attached is the ERP waves based on four-subject preprocessed data. The only difference between them is whether the EOG channels were included in ICA. ICALabel was used to automatically remove the EOG components.

Dear all,

Is there a way to convert partial eta^2 to cohen's d for repeated measures designs? I ask because I am conducting a meta-analysis and need to convert the studies' results into a common effect size.

I read that it can be calculated in 2 steps: first cohen's f and then cohen's d.

(1) cohen's f can be calculated from partial eta^2 as follows:

cohen's f = sqrt(partialeta^2/1-partialeta^2)

(2) cohen's f can be converted to cohen's d as follows:

cohen's d = f*2

When I try this with an example from a paper in which there was a partial eta^2 of .42, I get the following:

f=.85

d=1.7

However, I also have the pre and post means and SDs, and when I calculate the cohen's drm according to Lakens (2013), I get very different values. (Note that I do not have the r-value so I will assume one of .5). Mpre = .92, SDpre = .09, Mpost = .98, SDpost = .02.

Cohen's drm = (Mdiff/sqrt(SDpre^2 + SDpost^2 - 2 * r * SDpre * SDpost)) * sqrt(2(1-r))

drm = .73.

This is a huge difference. Even when changing the r-values, this effect size does not get close to the d-value of 1.7 estimated from the partial eta^2 value.

As such, I presume that the calculation I used from partial eta^2 to cohen's d is incorrect for a repeated measures design, and that a correction needs to be applied at step 1 or 2 (or both). I have not been able to find information about this so far. Does anyone know how to do this conversion for repeated measures designs properly?

Thank you in advance for your help.

Hi all,

I have this doubt that I could not solve by myself. I'm performing time-frequency analysis with EEGlab software for my master thesis and I'm not sure whether it is more correct to perform ICA and epoching in a precise order. I found some reccomendations in EEGLAB tutorial manual saying that it is advisable to perform ICA on a greater amount of data as possible. However, if I run ICA on EEGLAB before epoching, I cannot have a good visualisation of the ICA components (only the maps and components scroll). Instead, I have seen in a tutorial by Mike Cohen, that he performs epoching before ICA, so there is the possiblity to look at each component latency and frequency before rejecting. Personally, I found myself better with the second method, but I am not sure wheather it is correct or not.

I hope you can help me in this dilemma! Thank you!