martinwimpff / channel-attention Goto Github PK

hi @martinwimpff , thank you for your work and code.
While studying your and others models, I noticed the way of preprocessing datasets (StandardScaler method):

also you can find this scale method in other models:

ATCNet:
https://github.com/Altaheri/EEG-ATCNet/blob/12a4a53c3262a899b3e6b5856d5a9ef9eac6282b/preprocess.py#L128-L136

TCNet:
https://github.com/iis-eth-zurich/eeg-tcnet/blob/363c7e52d19285b0bac9a8846bbdbd6aac5aa851/sample_train.py#L50-L54

From my understanding, this method(StandardScaler: Doc) normalizes the columns of a 2D matrix, it centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set.

But for EEG dataset (shape: [trials, times] for each channel), it calculates the mean and std of all signal of trials at time sample t and standardizes it. However, this will change the time sequence of one trial , and maybe loss of time domain information?(because this changes the signal at t only according other trials).

So I can not understand this standard method, because the EEG time sequence is not like the multi-channel features.

Thank you again.😊