deploy: braindecode/braindecode@0061a05

Author: bruAristimunha

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "# .. note::\n#\n#     For cropped decoding, the above training setup is mathematically\n#     identical to sampling crops in your dataset, pushing them through the\n#     network and training directly on the individual crops. At the same time,\n#     the above training setup is much faster as it avoids redundant\n#     computations by using dilated convolutions, see our paper\n#     `Deep learning with convolutional neural networks for EEG decoding and visualization <https://arxiv.org/abs/1703.05051>`_. # noqa: E501\n#     However, the two setups are only mathematically identical in case (1)\n#     your network does not use any padding or only left padding and\n#     (2) your loss function leads\n#     to the same gradients when using the averaged output. The first is true\n#     for our shallow and deep ConvNet models and the second is true for the\n#     log-softmax outputs and negative log likelihood loss that is typically\n#     used for classification in PyTorch.\n#\n\n# Loading and preprocessing the dataset\n# -------------------------------------\n#\n# Loading and preprocessing stays the same as in the `Trialwise decoding\n# tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__.\n\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor, scale)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(scale, factor=1e6, apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "# .. note::\n#\n#     For cropped decoding, the above training setup is mathematically\n#     identical to sampling crops in your dataset, pushing them through the\n#     network and training directly on the individual crops. At the same time,\n#     the above training setup is much faster as it avoids redundant\n#     computations by using dilated convolutions, see our paper\n#     `Deep learning with convolutional neural networks for EEG decoding and visualization <https://arxiv.org/abs/1703.05051>`_. # noqa: E501\n#     However, the two setups are only mathematically identical in case (1)\n#     your network does not use any padding or only left padding and\n#     (2) your loss function leads\n#     to the same gradients when using the averaged output. The first is true\n#     for our shallow and deep ConvNet models and the second is true for the\n#     log-softmax outputs and negative log likelihood loss that is typically\n#     used for classification in PyTorch.\n#\n\n# Loading and preprocessing the dataset\n# -------------------------------------\n#\n# Loading and preprocessing stays the same as in the `Trialwise decoding\n# tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__.\n\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {

master/_downloads/1224801107d6c3b138edf8ee33de0561/plot_bcic_iv_2a_moabb_cropped.ipynb

@@ -39,17 +39,20 @@
 #
 
 from braindecode.preprocessing import (
-    exponential_moving_standardize, preprocess, Preprocessor, scale)
+    exponential_moving_standardize, preprocess, Preprocessor)
+from numpy import multiply
 
 low_cut_hz = 4.  # low cut frequency for filtering
 high_cut_hz = 38.  # high cut frequency for filtering
 # Parameters for exponential moving standardization
 factor_new = 1e-3
 init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
 
 preprocessors = [
     Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
-    Preprocessor(scale, factor=1e6, apply_on_array=True),  # Convert from V to uV
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
     Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
     Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
                  factor_new=factor_new, init_block_size=init_block_size)

master/_downloads/35f097f0d5a2df80f3fc3d21411435e1/plot_data_augmentation.py

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing.preprocess import preprocess, Preprocessor, scale\n\nhigh_cut_hz = 30\n\npreprocessors = [\n    Preprocessor(scale, factor=1e6, apply_on_array=True),\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz, n_jobs=n_jobs)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing.preprocess import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz, n_jobs=n_jobs)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {

master/_downloads/3e0455c88adcaa2571230abb981020db/plot_relative_positioning.ipynb

@@ -61,17 +61,20 @@
 #
 
 from braindecode.preprocessing import (
-    exponential_moving_standardize, preprocess, Preprocessor, scale)
+    exponential_moving_standardize, preprocess, Preprocessor)
+from numpy import multiply
 
 low_cut_hz = 4.  # low cut frequency for filtering
 high_cut_hz = 38.  # high cut frequency for filtering
 # Parameters for exponential moving standardization
 factor_new = 1e-3
 init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
 
 preprocessors = [
     Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
-    Preprocessor(scale, factor=1e6, apply_on_array=True),  # Convert from V to uV
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
     Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
     Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
                  factor_new=factor_new, init_block_size=init_block_size)

master/_downloads/475edf2d5ed60b15717de3100f3551a5/plot_bcic_iv_2a_moabb_trial.py

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import preprocess, Preprocessor, scale\n\nhigh_cut_hz = 30\n\npreprocessors = [\n    Preprocessor(scale, factor=1e6, apply_on_array=True),\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -72,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor, scale)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(scale, factor=1e6, apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {

master/_downloads/7132d1f42727a7844635064390fdd264/plot_sleep_staging_eldele2021.ipynb

@@ -65,12 +65,15 @@
 # Next, we preprocess the raw data. We convert the data to microvolts and apply
 # a lowpass filter.
 
-from braindecode.preprocessing import preprocess, Preprocessor, scale
+from braindecode.preprocessing import preprocess, Preprocessor
+from numpy import multiply
 
 high_cut_hz = 30
+# Factor to convert from V to uV
+factor = 1e6
 
 preprocessors = [
-    Preprocessor(scale, factor=1e6, apply_on_array=True),
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
     Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
 ]
 

master/_downloads/748680401b0360239d00624375dd9587/plot_bcic_iv_2a_moabb_trial.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor, scale)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(scale, factor=1e6, apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors)"
       ]
     },
     {