[Example] P300-VR dataset

docs/source/whats_new.rst

@@ -20,11 +20,13 @@ Enhancements
 
 - Adding second deployment of the documentation (:gh:`374` by `Bruno Aristimunha`_)
 - Adding Parallel evaluation for :func:`moabb.evaluations.WithinSessionEvaluation` , :func:`moabb.evaluations.CrossSessionEvaluation` (:gh:`364` by `Bruno Aristimunha`_)
+- Add example with VirtualReality BrainInvaders dataset (:gh:`393` by `Gregoire Cattan`_ and `Pedro L. C. Rodrigues`_)
 
 Bugs
 ~~~~
 
 - Restore 3 subject from Cho2017 (:gh:`392` by `Igor Carrara`_ and `Sylvain Chevallier`_)
+- Correct downloading with VirtualReality BrainInvaders dataset (:gh:`393` by `Gregoire Cattan`_)
 
 API changes
 ~~~~~~~~~~~

examples/vr_pc_p300_different_epoch_size.py

@@ -0,0 +1,93 @@
+"""
+=============================
+Classification of the trials
+=============================
+
+This example shows how to extract the epochs from the P300-VR dataset of a given
+subject and then classify them using Riemannian Geometry framework for BCI.
+We compare the scores in the VR and PC conditions, using different epoch size.
+
+"""
+# Authors: Pedro Rodrigues <[email protected]>
+# Modified by: Gregoire Cattan <[email protected]>
+# License: BSD (3-clause)
+
+import warnings
+
+import numpy as np
+import pandas as pd
+from pyriemann.classification import MDM
+from pyriemann.estimation import ERPCovariances
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import KFold
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import LabelEncoder
+from tqdm import tqdm
+
+from moabb.datasets import VirtualReality
+from moabb.paradigms import P300
+
+
+warnings.filterwarnings("ignore")
+
+# create dataset
+dataset = VirtualReality()
+
+# To encode classes into 0 and 1.
+le = LabelEncoder().fit(["Target", "NonTarget"])
+
+# get the paradigm
+paradigm = P300()
+
+# change this to include more subjects
+nsubjects = 2
+
+scores = []
+for tmax in [0.2, 1.0]:
+    paradigm.tmax = tmax
+
+    for subject in tqdm(dataset.subject_list[:nsubjects]):
+        scores_subject = [tmax, subject]
+
+        for condition in ["VR", "PC"]:
+            print(f"subject {subject}, {condition}, tmax {tmax}")
+
+            # define the dataset instance
+            dataset.virtual_reality = condition == "VR"
+            dataset.personal_computer = condition == "PC"
+
+            # cross validate with 3-folds validation.
+            kf = KFold(n_splits=3)
+
+            # There is 12 blocks of 5 repetitions.
+            repetitions = [1, 2]  # Select the first two repetitions.
+            blocks = np.arange(1, 12 + 1)
+
+            auc = []
+
+            for train_idx, test_idx in kf.split(np.arange(12)):
+                # split in training and testing blocks
+                X_train, y_train, _ = dataset.get_block_repetition(
+                    paradigm, [subject], blocks[train_idx], repetitions
+                )
+
+                X_test, y_test, _ = dataset.get_block_repetition(
+                    paradigm, [subject], blocks[test_idx], repetitions
+                )
+
+                pipe = make_pipeline(ERPCovariances(estimator="lwf"), MDM())
+                pipe.fit(X_train, y_train)
+                y_pred = pipe.predict(X_test)
+
+                y_test = le.transform(y_test)
+                y_pred = le.transform(y_pred)
+
+                auc.append(roc_auc_score(y_test, y_pred))
+
+            # stock scores
+            scores_subject.append(np.mean(auc))
+
+        scores.append(scores_subject)
+
+df = pd.DataFrame(scores, columns=["tmax", "subject", "VR", "PC"])
+print(df)

moabb/datasets/braininvaders.py

@@ -4,6 +4,7 @@
 import shutil
 import zipfile as z
 from distutils.dir_util import copy_tree
+from warnings import warn
 
 import mne
 import numpy as np
@@ -150,7 +151,7 @@ def _bi_get_subject_data(ds, subject):  # noqa: C901
             stim[idx_nontarget] = 1
             X = np.concatenate([S, stim[None, :]])
             sfreq = 512
-        elif ds.code == "Virtual Reality dataset":
+        elif ds.code == "P300-VR":
             data = loadmat(os.path.join(file_path, os.listdir(file_path)[0]))["data"]
 
             chnames = [
@@ -187,7 +188,7 @@ def _bi_get_subject_data(ds, subject):  # noqa: C901
             verbose=False,
         )
 
-        if not ds.code == "Virtual Reality dataset":
+        if not ds.code == "P300-VR":
             raw = mne.io.RawArray(data=X, info=info, verbose=False)
             raw.set_montage(make_standard_montage("standard_1020"))
 
@@ -388,15 +389,16 @@ def _bi_data_path(  # noqa: C901
             )
             for i in range(1, 5)
         ]
-    elif ds.code == "Virtual Reality dataset":
+    elif ds.code == "P300-VR":
         subject_paths = []
-        url = "{:s}subject_{:02d}_{:s}.mat".format(
-            VIRTUALREALITY_URL,
-            subject,
-            "VR" if ds.virtual_reality else ds.personal_computer,
-        )
-        file_path = dl.data_path(url, "VIRTUALREALITY")
-        subject_paths.append(file_path)
+        if ds.virtual_reality:
+            url = "{:s}subject_{:02d}_{:s}.mat".format(VIRTUALREALITY_URL, subject, "VR")
+            file_path = dl.data_path(url, "VIRTUALREALITY")
+            subject_paths.append(file_path)
+        if ds.personal_computer:
+            url = "{:s}subject_{:02d}_{:s}.mat".format(VIRTUALREALITY_URL, subject, "PC")
+            file_path = dl.data_path(url, "VIRTUALREALITY")
+            subject_paths.append(file_path)
 
     return subject_paths
 
@@ -868,6 +870,10 @@ def __init__(self, virtual_reality=False, screen_display=True):
 
         self.virtual_reality = virtual_reality
         self.personal_computer = screen_display
+        if not self.virtual_reality and not self.personal_computer:
+            warn(
+                "[P300-VR dataset] virtual_reality and screen display are False. No data will be downloaded, unless you change these parameters after initialization."
+            )
 
     def _get_single_subject_data(self, subject):
         """return data for a single subject"""
@@ -880,7 +886,7 @@ def data_path(
 
     def get_block_repetition(self, paradigm, subjects, block_list, repetition_list):
         """Select data for all provided subjects, blocks and repetitions.
-        Each subject has 5 blocks of 12 repetitions.
+        Each subject has 12 blocks of 5 repetitions.
 
         The returned data is a dictionary with the folowing structure::
 

moabb/tests/datasets.py

@@ -76,6 +76,17 @@ def __init__(self, *args, **kwargs):
     def test_canary(self):
         assert VirtualReality() is not None
 
+    def test_warning_if_parameters_false(self):
+        with self.assertWarns(UserWarning):
+            VirtualReality(virtual_reality=False, screen_display=False)
+
+    def test_data_path(self):
+        ds = VirtualReality(virtual_reality=True, screen_display=True)
+        data_path = ds.data_path(1)
+        assert len(data_path) == 2
+        assert "subject_01_VR.mat" in data_path[0]
+        assert "subject_01_PC.mat" in data_path[1]
+
     def test_get_block_repetition(self):
         ds = FakeVirtualRealityDataset()
         subject = 5