Comments (1)
eeyhsong
commented on July 27, 2024
hello @ll643989,
Sorry for the late reply.
- I haven't performed detailed experiments for the parameters of the optimizer. To stabilize the training processing, you can try different batch sizes, learning rates, beta1, beta2, etc.
- CSP is an excellent way to separate non-stationary time series such as EEG. However, you know we commonly use end-to-end manner in constructing deep learning methods. So we discard the CSP module in Conformer. But I believe combining new DL methods and traditional ML strategies would be better.
from eeg-conformer.
Related Issues (20)
- Missing SoftMax function HOT 2
- looking forward use it on gameemo datasets, open the whole code about it. thanks HOT 1
- train_data.npy in CAT.py HOT 2
- band-pass filter and augumentation for Dataset I (Dataset 2a) HOT 1
- How should 'Label' look? HOT 1
- promote on dataset1(Dataset 2a) S05 HOT 2
- nice work
- Regarding target category in visualization scripts HOT 4
- About the size of the model on a different dataset HOT 2
- Some question about data Segmentation and Reconstruction (S&R) data augmentation
- Invite do add your model to braindecode HOT 5
- Is it possible to get a pretrained model for transfer learning?
- how do i run inference on this model? HOT 1
- Reproducing the results of Graz data set A, 4 class motor imagery HOT 1
- how can i deal with seed dataset, any example? HOT 1
- 2b dataset
- 兄弟，用用参数吧，球球了 HOT 3
- Intentionally Calculating this for each forward ?? HOT 1
- KeyError: 'label' HOT 3
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from eeg-conformer.