[WIP] Adding a A large EEG database with users' profile information for motor imagery Brain-Computer Interface dataset

docs/source/datasets.rst

@@ -21,6 +21,9 @@ Motor Imagery Datasets
     BNCI2015001
     BNCI2015004
     Cho2017
+    Dreyer2023A
+    Dreyer2023B
+    Dreyer2023C
     Lee2019_MI
     MunichMI
     Ofner2017

docs/source/whats_new.rst

@@ -28,6 +28,7 @@ Enhancements
 - Adding saving option for the models (:gh:`401` by `Bruno Aristimunha`_ and `Igor Carrara`_)
 - Adding example to load different type of models (:gh:`401` by `Bruno Aristimunha`_ and `Igor Carrara`_)
 - Add resting state paradigm with dataset and example (:gh:`400` by `Gregoire Cattan`_ and `Pedro L. C. Rodrigues`_)
+- Add Dreyer 2023 Left-Right Motor Imagery dataset (:gh:`404` by `Sara Sedlar`_ and `Sylvain Chevallier`_)
 
 Bugs
 ~~~~

examples/plot_Dreyer_clf_scores_vs_subj_info.py

@@ -0,0 +1,134 @@
+"""
+===============================================
+Examples of analysis of a Dreyer2023 A dataset.
+===============================================
+
+This example shows how to plot Dreyer2023A Left-Right Imagery ROC AUC scores
+obtained with CSP+LDA pipeline versus demographic information of the examined
+subjects (gender and age) and experimenters (gender).
+
+To reduce computational time, the example is provided for four subjects.
+
+"""
+# Authors: Sara Sedlar <sara.sedlar@gmail.com>
+#          Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>
+# License: BSD (3-clause)
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sb
+from pyriemann.estimation import Covariances
+from pyriemann.spatialfilters import CSP
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.pipeline import make_pipeline
+
+from moabb.datasets import Dreyer2023A
+from moabb.evaluations import WithinSessionEvaluation
+from moabb.paradigms import MotorImagery
+
+
+# 1. Defining dataset, selecting subject for analysis and getting data
+########################################################################################
+dreyer2023 = Dreyer2023A()
+dreyer2023.subject_list = [1, 5, 7, 45]
+dreyer2023.get_data()
+########################################################################################
+# 2. Defining MotorImagery paradigm and CSP+LDA pipeline
+paradigm = MotorImagery()
+pipelines = {}
+pipelines["CSP+LDA"] = make_pipeline(
+    Covariances(estimator="oas"), CSP(nfilter=6), LDA(solver="lsqr", shrinkage="auto")
+)
+########################################################################################
+# 3. Within session evaluation of the pipeline
+evaluation = WithinSessionEvaluation(
+    paradigm=paradigm, datasets=[dreyer2023], suffix="examples", overwrite=False
+)
+results = evaluation.process(pipelines)
+results = results.loc[results["subject"].isin([str(s) for s in dreyer2023.subject_list])]
+########################################################################################
+# 4. Loading dataset info and concatenation with the obtained results
+info = dreyer2023.get_subject_info().rename(columns={"score": "score_MR"})
+results_info = pd.concat([info, results], axis=1)
+results_info["Age"] = 2019 - results_info["Birth_year"]
+########################################################################################
+# 5.1 Plotting subject AUC ROC scores vs subject's gender
+fig, ax = plt.subplots(nrows=2, ncols=2, facecolor="white", figsize=[16, 8], sharey=True)
+fig.subplots_adjust(wspace=0.0, hspace=0.5)
+sb.stripplot(
+    data=results_info,
+    y="score",
+    x="SUJ_gender",
+    ax=ax[0, 0],
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sb.pointplot(data=results_info, y="score", x="SUJ_gender", ax=ax[0, 0], palette="Set1")
+ax[0, 0].set_title("AUC ROC scores vs. subject gender")
+ax[0, 0].set_xticklabels(["Man", "Woman"])
+ax[0, 0].set_ylabel("ROC AUC")
+ax[0, 0].set_xlabel(None)
+ax[0, 0].set_ylim(0.3, 1)
+########################################################################################
+# 5.2 Plotting subject AUC ROC scores vs experimenter's gender
+sb.regplot(
+    data=results_info[results_info["SUJ_gender"] == 1][["score", "Age"]].astype(
+        "float32"
+    ),
+    y="score",
+    x="Age",
+    ax=ax[0, 1],
+    scatter_kws={"color": "#e41a1c", "alpha": 0.5},
+    line_kws={"color": "#e41a1c"},
+)
+sb.regplot(
+    data=results_info[results_info["SUJ_gender"] == 2][["score", "Age"]].astype(
+        "float32"
+    ),
+    y="score",
+    x="Age",
+    ax=ax[0, 1],
+    scatter_kws={"color": "#377eb8", "alpha": 0.5},
+    line_kws={"color": "#377eb8"},
+)
+ax[0, 1].set_title("AUC ROC scores vs. subject age per gender")
+ax[0, 1].set_ylabel(None)
+ax[0, 1].set_xlabel(None)
+red_man = mpatches.Patch(color="#e41a1c", label="Man")
+blue_woman = mpatches.Patch(color="#377eb8", label="Woman")
+ax[0, 1].legend(handles=[red_man, blue_woman])
+########################################################################################
+# 5.3 Plotting subject AUC ROC scores vs experimenter's gender
+sb.stripplot(
+    data=results_info,
+    y="score",
+    x="EXP_gender",
+    ax=ax[1, 0],
+    jitter=True,
+    alpha=0.5,
+    zorder=1,
+    palette="Set1",
+)
+sb.pointplot(data=results_info, y="score", x="EXP_gender", ax=ax[1, 0], palette="Set1")
+ax[1, 0].set_title("AUC ROC scores vs. experimenter gender")
+ax[1, 0].set_xticklabels(["Man", "Woman"])
+ax[1, 0].set_ylabel("ROC AUC")
+ax[1, 0].set_xlabel(None)
+ax[1, 0].set_ylim(0.3, 1)
+########################################################################################
+# 5.4 Plotting subject AUC ROC scores vs subject's age
+sb.regplot(
+    data=results_info[["score", "Age"]].astype("float32"),
+    y="score",
+    x="Age",
+    ax=ax[1, 1],
+    scatter_kws={"color": "black", "alpha": 0.5},
+    line_kws={"color": "black"},
+)
+ax[1, 1].set_title("AUC ROC scores vs. subject age")
+ax[1, 1].set_ylabel(None)
+########################################################################################
+plt.show()