deploy: NeuroTechX/moabb@a6e6815

Author: bruAristimunha

docs/_downloads/004be3e9d1a7fe3f376ebea76a062f80/4_adding_a_dataset.py

@@ -3,6 +3,7 @@
 Tutorial 4: Creating a dataset class
 ====================================
 """
+
 # Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
 #
 # https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019

docs/_downloads/0621540f2a81072e792d3fe5436855e1/noplot_phmd_ml_spectrum.ipynb

@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -10,6 +10,7 @@
 pipeline using a 5-fold cross-validation. The reported metric (here, accuracy)
 is the average of all fold.
 """
+
 # Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
 #
 # License: BSD (3-clause)

docs/_downloads/09c597e028c1574e907e103b3db53254/plot_within_session_ssvep.py

@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

docs/_downloads/11d7e3106c779d2d16f2ad6e45bde649/plot_braindecode.ipynb

@@ -7,6 +7,7 @@
 For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
 to easily scale to many datasets.
 """
+
 # Authors: Igor Carrara <igor.carrara@inria.fr>
 #
 # License: BSD (3-clause)

docs/_downloads/14eea0f23180ed6c51f0ac41b2f0162e/plot_benchmark_DL.py

@@ -255,7 +255,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

docs/_downloads/1cb0649d22103248d85dfccb151aabae/plot_disk_cache.ipynb

@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.18"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

docs/_downloads/1e19389137690794e1345c2b750ca301/noplot_bids_conversion.ipynb

@@ -16,6 +16,7 @@
 
 We will use the P300 paradigm, which uses the AUC as metric.
 """
+
 # Authors: Jan Sosulski
 #
 # License: BSD (3-clause)

docs/_downloads/1eb996e21dc5546952cdf5d77959e6ad/plot_learning_curve_p300_external.py

@@ -11,6 +11,7 @@
 generation, the results is computed on a local cluster but could be easily
 replicated on your infrastructure.
 """
+
 # Authors: Igor Carrara <igor.carrara@inria.fr>
 #          Bruno Aristimunha <b.aristimunha@gmail.com>
 #