EEG | Python toolbox for EEG analysis | Dataset library

I'm following the answer to this question and this scikit-learn tutorial to remove artifacts from an EEG signal. They seem simple enough, and I'm surely missing something obvious here.

The components extracted don't have the same length as my signal. I have 88 channels of several hours of recordings, so the shape of my signal matrix is (88, 8088516). Yet the output of ICA is (88, 88). In addition to being so short, each component seems to capture very large, noisy-looking deflections (so out of 88 components only a couple actually look like signal, the rest look like noise). I also would have expected only a few components to look noisy. I suspect I'm doing something wrong here?

The matrix of (channels x samples) has shape (88, 8088516).

Sample code (just using a random matrix for minimum working purposes):

I have a dataset in BrainVision Core Data Format which consists of the header file (.vhdr), marker file (.vmrk), and raw EEG data (.eeg) file for each subject. I know that python has mne.io.read_raw_brainvision() function which reads header file and returns a raw object containing BrainVision data. I do not know how to proceed after that or how can I read .eeg file. Thanks

I can't apply the alterations I make to dataframes inside a dictionary. The changes are done with a for loop.

The problem is that although the loop works because the single iterated df makes the changes, they do not apply to the dictionary they are in. The end goal is to create a merge of all the dataframes since they come from different excel sheets and sheets. Here the code:

1. Import the two excel files, assigning None to the Sheet_Name parameter in order to import all the sheets of the document into a dict. I have 8 sheet in EEG excel file and 5 in SC file

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So, I collected data from 21 participants with 16 EEG channels and I extracted the Gamma band. My current dataframe looks like this ([336 rows x 2 columns]):

Now I want to transpose it in such a way, that I have the gamma values for each channel in one column. Like this:

when I just transpose the dataframe, then I get one row with all channels next to each other:

I looked at pd.melt but I can't figure it out. Can someone help?

Thank you in advance!

I got this error when I try to fit the model. I tried to use a single GPU version but it remains. If I upgrade to TensorFlow 2 it will be solved but I need to keep it that in this version of TensorFlow.

This is the code for the model that I have used. This model consists of different layers.

I am trying to implement a Common Average Reference function in python. The idea is to compute the average of the signal at all EEG channels and subtract it from the EEG signal at every channels for every time point. The input of this function is a NumPy array called trials. Trials is a 3D array that contains EEG data in this form: (trials x time x channels). for example:

I have a CSV file with data (IDs) listed in one column and I added the extension to each ID via a for loop and then I would like to save the newly generated list in for loop to a CSV file. See the code. CSV file Data

Is it possible to define a function like plot_topography in MatLab to draw the Topographic EEG/MEG plot in Julia?

I am working with an FTDI device that has native software for Windows, but nothing available for Linux. I am trying to read data from the device using pylibftdi. I would like to translate C# code that is provided by the device manufacturer and purportedly works (unclear if this is true) but have not been successful. So far I have done the following:

1. Installed the Linux D2XX drivers based on these instructions. Installation was successful.

2. Followed the directions here and here to enable the FTDI device to connect to the Linux system.

3. After plugging the FTDI device into the Linux system USB port: