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[EHN] Inclusion of SincShallowNet - #678
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bruAristimunha merged 9 commits intoOct 26, 2024
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #678 +/- ##
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+ Coverage 87.13% 87.21% +0.07%
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Files 75 76 +1
Lines 6739 6812 +73
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+ Hits 5872 5941 +69
- Misses 867 871 +4 |
sylvchev
approved these changes
Oct 25, 2024
Co-authored-by: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>
bruAristimunha
commented
Feb 4, 2025
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| high_freqs = torch.clamp( | ||
| low_freqs + torch.abs(self.bandwidths), | ||
| min=self.min_freq, | ||
| max=self.sfreq / 2.0, | ||
| ) | ||
| bandwidths = high_freqs - low_freqs |
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normalization matters here.
Get the model after the training and before (check if the random stage initialization is the same).
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Already solved on EEGMiner.
braindecode/braindecode/models/eegminer.py
Line 388 in cf53ebc
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