Adding motor imagery part of the Lee2019 dataset (rebase) (#170)

* Adding motor imagery part of the Lee2019 datasets

* Correcting some length issues.

* Improving documentation of Lee2019.py. Adding Lee2019_MI in dataset.rst.

* pre-commit

* removed useless option

Co-authored-by: AliAbdulHussain <[email protected]>
Co-authored-by: Sylvain Chevallier <[email protected]>

Author: 3 people

docs/source/conf.py

@@ -74,7 +74,6 @@
 sphinx_gallery_conf = {
     "examples_dirs": ["../../examples", "../../tutorials"],
     "gallery_dirs": ["auto_examples", "auto_tutorials"],
-    "backreferences_dir": False,
 }
 
 # Add any paths that contain templates here, relative to this directory.

docs/source/datasets.rst

@@ -21,6 +21,7 @@ Motor Imagery Datasets
     BNCI2015001
     BNCI2015004
     Cho2017
+    Lee2019_MI
     MunichMI
     Ofner2017
     PhysionetMI

moabb/datasets/Lee2019.py

@@ -0,0 +1,136 @@
+"""
+BMI/OpenBMI dataset (Motor Imagery).
+"""
+
+import numpy as np
+from mne import create_info
+from mne.channels import make_standard_montage
+from mne.io import RawArray
+from scipy.io import loadmat
+
+from moabb.datasets import download as dl
+from moabb.datasets.base import BaseDataset
+
+
+Lee2019_URL = "ftp://parrot.genomics.cn/gigadb/pub/10.5524/100001_101000/100542/"
+
+
+class Lee2019_MI(BaseDataset):
+    """Motor Imagery BMI/OpenBMI dataset from BMI/OpenBMI dataset.
+
+    Dataset from Lee et al 2019 [1]_.
+
+    **Dataset Description**
+
+    EEG signals were recorded with a sampling rate of 1,000 Hz and
+    collected with 62 Ag/AgCl electrodes. The EEG amplifier used
+    in the experiment was a BrainAmp (Brain Products; Munich,
+    Germany). The channels were nasion-referenced and grounded
+    to electrode AFz. Additionally, an EMG electrode recorded from
+    each flexor digitorum profundus muscle with the olecranon
+    used as reference. The EEG/EMG channel configuration and
+    indexing numbers are described in Fig. 1. The impedances of the
+    EEG electrodes were maintained below 10 k during the entire
+    experiment.
+
+    MI paradigm
+    The MI paradigm was designed following a well-established system protocol.
+    For all blocks, the first 3 s of each trial began
+    with a black fixation cross that appeared at the center of the
+    monitor to prepare subjects for the MI task. Afterwards, the subject
+    performed the imagery task of grasping with the appropriate
+    hand for 4 s when the right or left arrow appeared as a visual cue.
+    After each task, the screen remained blank for 6 s (± 1.5 s). The
+    experiment consisted of training and test phases; each phase
+    had 100 trials with balanced right and left hand imagery tasks.
+    During the online test phase, the fixation cross appeared at the
+    center of the monitor and moved right or left, according to the
+    real-time classifier output of the EEG signal.
+
+    References
+    ----------
+    .. [1] Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E.,
+           Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI
+           toolbox for three BCI paradigms: An investigation into BCI
+           illiteracy. GigaScience, 8(5), 1–16.
+           https://doi.org/10.1093/gigascience/giz002
+    """
+
+    def __init__(self):
+        super().__init__(
+            subjects=list(range(1, 55)),
+            sessions_per_subject=2,
+            events=dict(left_hand=2, right_hand=1),
+            code="Lee2019_MI",
+            interval=[0, 4],
+            paradigm="imagery",
+            doi="10.5524/100542",
+        )
+
+    def _get_single_subject_data(self, subject):
+        """return data for a single subejct"""
+
+        sessions = {}
+        file_path_list = self.data_path(subject)
+
+        for session in range(1, 3):
+            data = loadmat(file_path_list[session - 1])
+
+            # Create channel info and montage
+            eeg_ch_names = data["EEG_MI_train"][0, 0][8][0]
+            ch_names = [elem[0] for elem in eeg_ch_names] + ["stim"]
+            ch_types = ["eeg"] * 62 + ["stim"]
+            sfreq = data["EEG_MI_train"][0, 0][3][0, 0]
+            info = create_info(ch_names=ch_names, ch_types=ch_types, sfreq=sfreq)
+            montage = make_standard_montage("standard_1005")
+
+            # Create raw_data
+            raw_train_data = np.transpose(data["EEG_MI_train"][0, 0][0], (1, 2, 0))
+            raw_test_data = np.transpose(data["EEG_MI_test"][0, 0][0], (1, 2, 0))
+            raw_data = np.concatenate([raw_train_data, raw_test_data], axis=0)
+
+            # Create raw_event
+            train_event_id = data["EEG_MI_train"][0, 0][4].ravel()
+            test_event_id = data["EEG_MI_test"][0, 0][4].ravel()
+            event_id = np.concatenate([train_event_id, test_event_id], axis=0)
+            raw_events = np.zeros((raw_data.shape[0], 1, raw_data.shape[2]))
+            raw_events[:, 0, 0] = event_id
+
+            # Zero pad the data
+            data = np.concatenate([raw_data, raw_events], axis=1)
+            zeroshape = (data.shape[0], data.shape[1], 50)
+            data = np.concatenate(
+                [np.zeros(zeroshape), data, np.zeros(zeroshape)], axis=2
+            )
+
+            # Create RawArray
+            raw = RawArray(
+                data=np.concatenate(list(data), axis=1), info=info, verbose=False
+            )
+            raw.set_montage(montage)
+
+            # add the data to sessions
+            session_name = "session_{}".format(session)
+            sessions[session_name] = {"run_1": raw}
+
+        return sessions
+
+    def data_path(
+        self, subject, path=None, force_update=False, update_path=None, verbose=None
+    ):
+
+        if subject not in self.subject_list:
+            raise (ValueError("Invalid subject number"))
+
+        subject_paths = []
+        for session in range(1, 3):
+            url = "{0}session{1}/s{2}/sess{1:02d}_subj{2:02d}_EEG_MI.mat".format(
+                Lee2019_URL, session, subject
+            )
+
+            data_path = dl.data_path(
+                url, "Lee2019_MI", path, force_update, update_path, verbose
+            )
+            subject_paths.append(data_path)
+
+        return subject_paths

moabb/datasets/__init__.py

@@ -21,6 +21,7 @@
 
 # flake8: noqa
 from .gigadb import Cho2017
+from .Lee2019 import Lee2019_MI
 from .mpi_mi import MunichMI
 from .physionet_mi import PhysionetMI
 from .schirrmeister2017 import Schirrmeister2017