Update dataset info

docs/source/dataset_summary.rst

@@ -17,25 +17,25 @@ Motor Imagery
 ======================
 
 .. csv-table::
-   :header: Dataset, #Subj, #Chan, #Classes, #Trials, len, Sampling rate, #Sessions, #Trials*#Sessions
+   :header: Dataset, #Subj, #Chan, #Classes, #Trials, Trial length, Freq, #Session, #Runs, Total_trials
    :class: sortable
 
-   AlexMI,8,16,3,20,3s,512Hz,1,20
-   BNCI2014001,10,22,4,144,4s,250Hz,2,288
-   BNCI2014002,15,15,2,80,5s,512Hz,1,80
-   BNCI2014004,10,3,2,360,4.5s,250Hz,5,1800
-   BNCI2015001,13,13,2,200,5s,512Hz,2,400
-   BNCI2015004,10,30,5,80,7s,256Hz,2,160
-   Cho2017,53,64,2,100,3s,512Hz,1,100
-   Lee2019_MI,55,62,2,100,4s,1000Hz,2,200
-   MunichMI,10,128,2,150,7s,500Hz,1,150
-   Schirrmeister2017,14,128,4,120,4s,500Hz,1,120
-   Ofner2017,15,61,7,60,3s,512Hz,1,60
-   PhysionetMI,109,64,4,23,3s,160Hz,1,23
-   Shin2017A,29,30,2,30,10s,200Hz,3,90
-   Shin2017B,29,30,2,30,10s,200Hz,3,90
-   Weibo2014,10,60,7,80,4s,200Hz,1,80
-   Zhou2016,4,14,3,160,5s,250Hz,3,480
+        AlexMI,8,16,3,20,3s,512Hz,1,1,480
+        BNCI2014001,9,22,4,144,4s,250Hz,2,6,62208
+        BNCI2014002,14,15,2,80,5s,512Hz,1,8,17920
+        BNCI2014004,9,3,2,360,4.5s,250Hz,5,1,32400
+        BNCI2015001,12,13,2,200,5s,512Hz,3,1,14400
+        BNCI2015004,9,30,5,80,7s,256Hz,2,1,7200
+        Cho2017,52,64,2,100,3s,512Hz,1,1,9800
+        Lee2019_MI,55,62,2,100,4s,1000Hz,2,1,11000
+        MunichMI,10,128,2,150,7s,500Hz,1,1,3000
+        Schirrmeister2017,14,128,4,120,4s,500Hz,1,2,13440
+        Ofner2017,15,61,7,60,3s,512Hz,1,10,63000
+        PhysionetMI,109,64,4,23,3s,160Hz,1,1,69760
+        Shin2017A,29,30,2,30,10s,200Hz,3,1,5220
+        Shin2017B,29,30,2,30,10s,200Hz,3,1,5220
+        Weibo2014,10,60,7,80,4s,200Hz,1,1,5600
+        Zhou2016,4,14,3,160,5s,250Hz,3,2,11496
 
 P300/ERP
 ======================
@@ -49,10 +49,10 @@ P300/ERP
    BNCI2015003, 10, 8, 1500 NT / 300 T, 0.8s, 256Hz, 1
    bi2012, 25, 16, 6140 NT / 128 T, 1s, 512Hz, 2
    bi2013a, 24, 16, 3200 NT / 640 T, 1s, 512Hz, 8 for subjects 1-7 else 1
-   bi2014a, 71, 16, , 1s, 512Hz, up to 3
-   bi2014b, 38, 32, , 1s, 512Hz, 3
-   bi2015a, 50, 32, , 1s, 512Hz, 3
-   bi2015b, 44, 32, , 1s, 512Hz, 2
+   bi2014a, 64, 16, 990 NT / 198 T, 1s, 512Hz, up to 3
+   bi2014b, 37, 32, 200 NT / 40 T, 1s, 512Hz, 3
+   bi2015a, 43, 32, 4131 NT / 825 T, 1s, 512Hz, 3
+   bi2015b, 44, 32, 2160 NT / 480 T, 1s, 512Hz, 2
    VirtualReality, 24, 16, 600 NT / 120 T, 1s, 512Hz, 2
    Huebner2017, 13, 31, , 0.9s, 1000Hz, 1
    Huebner2018, 12, 31, , 0.9s, 1000Hz, 1
@@ -68,13 +68,13 @@ SSVEP
    :header: Dataset, #Subj, #Chan, #Classes, #Trials / class, Trials length, Sampling rate, #Sessions
    :class: sortable
 
-   Lee2019_SSVEP,24,16,4,25,1s,1000Hz,1
+   Lee2019_SSVEP,54,16,4,25,1s,1000Hz,1
    SSVEPExo,12,8,4,16,2s,256Hz,1
    MAMEM1,10,256,5,12-15,3s,250Hz,1
    MAMEM2,10,256,5,20-30,3s,250Hz,1
    MAMEM3,10,14,4,20-30,3s,128Hz,1
    Nakanishi2015,9,8,12,15,4.15s,256Hz,1
-   Wang2016,32,62,40,6,5s,250Hz,1
+   Wang2016,34,62,40,6,5s,250Hz,1
 
 
 

docs/source/whats_new.rst

@@ -18,6 +18,8 @@ Develop branch
 Enhancements
 ~~~~~~~~~~~~
 
+- Adding new script to get the meta information of the datasets (:gh:`389` by `Bruno Aristimunha`_)
+- Fixing the dataset description based on the meta information (:gh:`389` by `Bruno Aristimunha`_)
 - 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`_)
 

moabb/datasets/Lee2019.py

@@ -395,7 +395,7 @@ class Lee2019_SSVEP(Lee2019):
         =============  =======  =======  ==========  =================  ===============  ===============  ===========
         Name             #Subj    #Chan    #Classes    #Trials / class  Trials length    Sampling rate      #Sessions
         =============  =======  =======  ==========  =================  ===============  ===============  ===========
-        Lee2019_SSVEP       24       16           4                 25  1s               1000Hz                     1
+        Lee2019_SSVEP       54       16           4                 25  1s               1000Hz                     1
         =============  =======  =======  ==========  =================  ===============  ===============  ===========
 
     Dataset from Lee et al 2019 [1]_.

moabb/datasets/bnci.py

@@ -686,7 +686,7 @@ class BNCI2014001(MNEBNCI):
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
         Name           #Subj    #Chan    #Classes    #Trials / class  Trials len    Sampling rate      #Sessions
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
-        BNCI2014001       10       22           4                144  4s            250Hz                      2
+        BNCI2014001       9       22           4                144  4s            250Hz                      2
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
 
     Dataset IIa from BCI Competition 4 [1]_.
@@ -750,7 +750,7 @@ class BNCI2014002(MNEBNCI):
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
         Name           #Subj    #Chan    #Classes    #Trials / class  Trials len    Sampling rate      #Sessions
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
-        BNCI2014002       15       15           2                 80  5s            512Hz                      1
+        BNCI2014002       14       15           2                 80  5s            512Hz                      1
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
 
     Motor Imagery Dataset from [1]_.
@@ -813,7 +813,7 @@ class BNCI2014004(MNEBNCI):
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
         Name           #Subj    #Chan    #Classes    #Trials / class  Trials len    Sampling rate      #Sessions
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
-        BNCI2014004       10        3           2                360  4.5s          250Hz                      5
+        BNCI2014004       9        3           2                360  4.5s          250Hz                      5
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
 
     Dataset B from BCI Competition 2008.
@@ -1030,7 +1030,7 @@ class BNCI2015001(MNEBNCI):
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
         Name           #Subj    #Chan    #Classes    #Trials / class  Trials len    Sampling rate      #Sessions
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
-        BNCI2015001       13       13           2                200  5s            512Hz                      2
+        BNCI2015001       12       13           2                200  5s            512Hz                      2
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
 
     Dataset from [1]_.
@@ -1131,7 +1131,7 @@ class BNCI2015004(MNEBNCI):
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
         Name           #Subj    #Chan    #Classes    #Trials / class  Trials len    Sampling rate      #Sessions
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
-        BNCI2015004       10       30           5                 80  7s            256Hz                      2
+        BNCI2015004       9       30           5                 80  7s            256Hz                      2
         ===========  =======  =======  ==========  =================  ============  ===============  ===========
 
     Dataset from [1]_.

moabb/datasets/braininvaders.py

@@ -576,7 +576,7 @@ class bi2014a(BaseDataset):
         ================ ======= ======= ================ =============== =============== ===========
          Name             #Subj   #Chan   #Trials/class    Trials length   Sampling Rate   #Sessions
         ================ ======= ======= ================ =============== =============== ===========
-         bi2014a           71      16        5 NT x 1 T         1s              512Hz       up to 3
+         bi2014a           64      16        5 NT x 1 T         1s              512Hz       up to 3
         ================ ======= ======= ================ =============== =============== ===========
 
     This dataset contains electroencephalographic (EEG) recordings of 71 subjects
@@ -634,7 +634,7 @@ class bi2014b(BaseDataset):
         ================ ======= ======= ================ =============== =============== ===========
          Name             #Subj   #Chan   #Trials/class    Trials length   Sampling Rate   #Sessions
         ================ ======= ======= ================ =============== =============== ===========
-         bi2014b           38      32        5 NT x 1 T         1s              512Hz           3
+         bi2014b           37      32        5 NT x 1 T         1s              512Hz           3
         ================ ======= ======= ================ =============== =============== ===========
 
     This dataset contains electroencephalographic (EEG) recordings of 38 subjects playing in
@@ -693,7 +693,7 @@ class bi2015a(BaseDataset):
         ================ ======= ======= ================ =============== =============== ===========
          Name             #Subj   #Chan   #Trials/class    Trials length   Sampling Rate   #Sessions
         ================ ======= ======= ================ =============== =============== ===========
-         bi2015a           50      32        5 NT x 1 T         1s              512Hz           3
+         bi2015a           43      32        5 NT x 1 T         1s              512Hz           3
         ================ ======= ======= ================ =============== =============== ===========
 
     This dataset contains electroencephalographic (EEG) recordings

moabb/datasets/gigadb.py

@@ -27,7 +27,7 @@ class Cho2017(BaseDataset):
         =======  =======  =======  ==========  =================  ============  ===============  ===========
         Name       #Subj    #Chan    #Classes    #Trials / class  Trials len    Sampling rate      #Sessions
         =======  =======  =======  ==========  =================  ============  ===============  ===========
-        Cho2017       53       64           2                100  3s            512Hz                      1
+        Cho2017       52       64           2                100  3s            512Hz                      1
         =======  =======  =======  ==========  =================  ============  ===============  ===========
 
     Dataset from the paper [1]_.

moabb/datasets/ssvep_wang.py

@@ -30,7 +30,7 @@ class Wang2016(BaseDataset):
         ========  =======  =======  ==========  =================  ===============  ===============  ===========
         Name        #Subj    #Chan    #Classes    #Trials / class  Trials length    Sampling rate      #Sessions
         ========  =======  =======  ==========  =================  ===============  ===============  ===========
-        Wang2016       32       62          40                  6  5s               250Hz                      1
+        Wang2016       34       62          40                  6  5s               250Hz                      1
         ========  =======  =======  ==========  =================  ===============  ===============  ===========
 
     Dataset from [1]_.

scripts/generating_metainfo.py

@@ -0,0 +1,194 @@
+from argparse import ArgumentParser
+from pathlib import Path
+
+import mne
+import numpy as np
+import pandas as pd
+
+import moabb
+from moabb.datasets.utils import dataset_search
+from moabb.utils import set_download_dir
+
+
+columns_name = [
+    "Dataset",
+    "#Subj",
+    "#Chan",
+    "#Classes",
+    "trials/events",
+    "Window Size (s)",
+    "Freq (Hz)",
+    "#Session",
+    "#Runs",
+    "Total_trials",
+]
+
+
+def parser_init():
+    parser = ArgumentParser(description="Getting the meta-information script for MOABB")
+
+    parser.add_argument(
+        "-mne_p",
+        "--mne_data",
+        dest="mne_data",
+        default=Path.home() / "mne_data",
+        type=Path,
+        help="Folder where to save and load the datasets with mne structure.",
+    )
+
+    return parser
+
+
+def process_trial_freq(trials_per_events, prdgm):
+    """
+    Function to process the trial frequency.
+    Getting the median value if the paradigm is MotorImagery.
+
+    Parameters
+    ----------
+    trials_per_events: dict
+    prdgm: str
+
+    Returns
+    -------
+    trial_freq: str
+    """
+    class_per_trial = list(trials_per_events.values())
+
+    if prdgm == "imagery" or prdgm == "ssvep":
+        return f"{int(np.median(class_per_trial))}"
+    elif prdgm == "p300":
+        not_target = max(trials_per_events.values())
+        target = min(trials_per_events.values())
+        return f"NT{not_target} / T {target}"
+
+
+def get_meta_info(dataset, dataset_name, paradigm, prdgm_name):
+    """
+    Function to get the meta-information of a dataset.
+
+    Parameters
+    ----------
+    dataset: BaseDataset
+        Dataset object
+    dataset_name: str
+        Dataset name
+    paradigm: BaseParadigm
+         Paradigm object to process the dataset
+    prdgm_name: str
+        Paradigm name
+
+    Returns
+    -------
+
+    """
+    subjects = len(dataset.subject_list)
+    session = dataset.n_sessions
+
+    X, _, metadata = paradigm.get_data(dataset, [1], return_epochs=True)
+
+    sfreq = int(X.info["sfreq"])
+    nchan = X.info["nchan"]
+    runs = len(metadata["run"].unique())
+    classes = len(X.event_id)
+    epoch_size = X.tmax - X.tmin
+
+    trials_per_events = mne.count_events(X.events)
+    total_trials = int(sum(trials_per_events.values()))
+    trial_class = process_trial_freq(trials_per_events, prdgm_name)
+
+    info_dataset = pd.Series(
+        [
+            dataset_name,
+            subjects,
+            nchan,
+            classes,
+            trial_class,
+            epoch_size,
+            sfreq,
+            session,
+            runs,
+            session * runs * total_trials * subjects,
+        ],
+        index=columns_name,
+    )
+
+    return info_dataset
+
+
+if __name__ == "__main__":
+    mne.set_log_level(False)
+
+    parser = parser_init()
+    options = parser.parse_args()
+    mne_path = Path(options.mne_data)
+
+    set_download_dir(mne_path)
+
+    paradigms = {}
+    paradigms["imagery"] = moabb.paradigms.MotorImagery()
+    paradigms["ssvep"] = moabb.paradigms.SSVEP()
+    paradigms["p300"] = moabb.paradigms.P300()
+
+    for prdgm_name, paradigm in paradigms.items():
+        dataset_list = dataset_search(paradigm=prdgm_name)
+
+        metainfo = []
+        for dataset in dataset_list:
+            dataset_name = str(dataset).split(".")[-1].split(" ")[0]
+
+            dataset_path = f"{mne_path.parent}/metainfo/metainfo_{dataset_name}.csv"
+
+            if not dataset_path.exists():
+                print(
+                    "Trying to get the meta information from the "
+                    f"dataset {dataset} with {prdgm_name}"
+                )
+
+                try:
+                    info_dataset = get_meta_info(
+                        dataset, dataset_name, paradigm, prdgm_name
+                    )
+                    print(
+                        "Saving the meta information for the dataset in the file: ",
+                        dataset_path,
+                    )
+                    info_dataset.to_csv(dataset_path)
+                    metainfo.append(info_dataset)
+
+                except Exception as ex:
+                    print(f"Error with {dataset} with {prdgm_name} paradigm", end=" ")
+                    print(f"Error: {ex}")
+
+                    if prdgm_name == "imagery":
+                        print("Trying with the LeftRightImagery paradigm")
+                        prdgm2 = moabb.paradigms.LeftRightImagery()
+                        try:
+                            info_dataset = get_meta_info(
+                                dataset, dataset_name, prdgm2, prdgm_name
+                            )
+                            print(
+                                "Saving the meta information for the dataset in the file: ",
+                                dataset_path,
+                            )
+                            info_dataset.to_csv(dataset_path)
+                            metainfo.append(info_dataset)
+
+                        except Exception as ex:
+                            print(
+                                f"Error with {dataset} with {prdgm_name} paradigm",
+                                end=" ",
+                            )
+                            print(f"Error: {ex}")
+            else:
+                print(f"Loading the meta information from {dataset_path}")
+                info_dataset = pd.read_csv(dataset_path)
+                metainfo.append(info_dataset)
+
+        paradigm_df = pd.concat(metainfo, axis=1).T
+
+        paradigm_df.columns = columns_name
+        dataset_path = mne_path.parent / "metainfo" / f"metainfo_{dataset_name}.csv"
+        print(f"Saving the meta information for the paradigm {dataset_path}")
+
+        paradigm_df.to_csv(dataset_path, index=None)