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Models

EEGResNet([n_chans, n_outputs, n_times, ...])

-

Residual Network for EEG.

+

Residual Network for EEG from Schirrmeister et al 2017.

TCN([n_chans, n_outputs, n_blocks, ...])

Temporal Convolutional Network (TCN) from Bai et al 2018.

diff --git a/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html index 1af091f0..d8142e4b 100644 --- a/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html +++ b/dev/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html @@ -596,7 +596,7 @@

Preprocessingpreprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1) - -plot bcic iv 4 ecog cropped

Total running time of the script: (2 minutes 11.471 seconds)

-

Estimated memory usage: 657 MB

+plot bcic iv 4 ecog cropped

Total running time of the script: (1 minutes 0.676 seconds)

+

Estimated memory usage: 824 MB

  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
 -------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
-      1            0.2535        1.6458       0.2535            0.2535        5.1252  0.0006  1.6759
-      2            0.2639        1.3297       0.2500            0.2500        5.7607  0.0005  1.4328
-      3            0.2639        1.1539       0.2500            0.2500        5.1231  0.0002  1.4704
-      4            0.2708        1.1567       0.2535            0.2535        4.2555  0.0000  1.4206
+      1            0.2535        1.6458       0.2535            0.2535        5.1252  0.0006  1.7671
+      2            0.2639        1.3297       0.2500            0.2500        5.7607  0.0005  1.5550
+      3            0.2639        1.1539       0.2500            0.2500        5.1231  0.0002  1.5500
+      4            0.2708        1.1567       0.2535            0.2535        4.2555  0.0000  1.5569
 
 <class 'braindecode.classifier.EEGClassifier'>[initialized](
   module_=============================================================================================================================================
@@ -839,8 +839,8 @@ 

Setting the data aug -

Total running time of the script: (0 minutes 15.684 seconds)

-

Estimated memory usage: 506 MB

+

Total running time of the script: (0 minutes 16.370 seconds)

+

Estimated memory usage: 706 MB

-
<braindecode.datasets.base.BaseConcatDataset object at 0x7ff0ccf7bfd0>
+
<braindecode.datasets.base.BaseConcatDataset object at 0x7fbad9589870>
 
@@ -865,31 +865,31 @@

Training
  epoch    train_acc    train_loss    valid_acc    valid_loss    cp     dur
 -------  -----------  ------------  -----------  ------------  ----  ------
-      1       0.5234        0.7013       0.6680        0.6320     +  1.0212
-      2       0.5938        0.7149       0.4880        0.8358        0.7739
-      3       0.4922        1.0040       0.6440        0.6172     +  0.7472
-      4       0.5234        0.7031       0.6120        0.5990     +  0.7302
-      5       0.5391        0.6751       0.5920        0.6213        0.7457
-      6       0.6719        0.6227       0.5920        0.6263        0.7459
-      7       0.6562        0.6309       0.6240        0.6117        0.7343
-      8       0.6641        0.6272       0.6480        0.5950     +  0.7299
-      9       0.6328        0.6238       0.6680        0.5797     +  0.7449
-     10       0.6406        0.6177       0.6800        0.5746     +  0.7320
-     11       0.6250        0.6323       0.7040        0.5787        0.7264
-     12       0.6094        0.6281       0.6760        0.5772        0.7413
-     13       0.6328        0.6422       0.6880        0.5790        0.7455
-     14       0.6406        0.5920       0.6840        0.5765        0.7348
-     15       0.6562        0.6170       0.6920        0.5730     +  0.7237
-     16       0.7578        0.5608       0.6960        0.5676     +  0.7441
-     17       0.6875        0.5936       0.7120        0.5612     +  0.7438
-     18       0.7734        0.5472       0.7080        0.5500     +  0.7272
-     19       0.7656        0.5245       0.7120        0.5400     +  0.7461
-     20       0.6641        0.5641       0.7160        0.5333     +  0.7406
-     21       0.7422        0.5307       0.7200        0.5272     +  0.7368
-     22       0.7109        0.5499       0.7360        0.5211     +  0.7302
-     23       0.6250        0.6259       0.7400        0.5164     +  0.7435
-     24       0.7031        0.5712       0.7400        0.5120     +  0.7502
-     25       0.7109        0.5030       0.7280        0.5120        0.7471
+      1       0.5234        0.7013       0.6680        0.6320     +  1.1686
+      2       0.5938        0.7149       0.4880        0.8358        0.9130
+      3       0.4922        1.0040       0.6440        0.6172     +  0.8862
+      4       0.5234        0.7031       0.6120        0.5990     +  0.8866
+      5       0.5391        0.6751       0.5920        0.6213        0.8853
+      6       0.6719        0.6227       0.5920        0.6263        0.8853
+      7       0.6562        0.6309       0.6240        0.6117        0.8836
+      8       0.6641        0.6272       0.6480        0.5950     +  0.8611
+      9       0.6328        0.6238       0.6680        0.5797     +  0.8842
+     10       0.6406        0.6177       0.6800        0.5746     +  0.8826
+     11       0.6250        0.6323       0.7040        0.5787        0.8748
+     12       0.6094        0.6281       0.6760        0.5772        0.8744
+     13       0.6328        0.6422       0.6880        0.5790        0.8755
+     14       0.6406        0.5920       0.6840        0.5765        0.8717
+     15       0.6562        0.6170       0.6920        0.5730     +  0.8717
+     16       0.7578        0.5608       0.6960        0.5676     +  0.8828
+     17       0.6875        0.5936       0.7120        0.5612     +  0.8848
+     18       0.7734        0.5472       0.7080        0.5500     +  0.8830
+     19       0.7656        0.5245       0.7120        0.5400     +  0.8767
+     20       0.6641        0.5641       0.7160        0.5333     +  0.9232
+     21       0.7422        0.5307       0.7200        0.5272     +  0.8864
+     22       0.7109        0.5499       0.7360        0.5211     +  0.8721
+     23       0.6250        0.6259       0.7400        0.5164     +  0.8739
+     24       0.7031        0.5712       0.7400        0.5120     +  0.8878
+     25       0.7109        0.5030       0.7280        0.5120        0.8986
 
@@ -1064,7 +1064,7 @@

Using the learned re plot relative positioning
/home/runner/work/braindecode/braindecode/examples/advanced_training/plot_relative_positioning.py:472: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
   colors = cm.get_cmap('viridis', 5)(range(5))
 
-<matplotlib.legend.Legend object at 0x7ff00e5f4910>
+<matplotlib.legend.Legend object at 0x7fbb96ba06d0>
 

We see that there is sleep stage-related structure in the embedding. A @@ -1121,8 +1121,8 @@

References -

Total running time of the script: (1 minutes 30.485 seconds)

-

Estimated memory usage: 123 MB

+

Total running time of the script: (1 minutes 39.262 seconds)

+

Estimated memory usage: 149 MB