Alphawaves dataset

docs/source/dataset_summary.rst

@@ -120,6 +120,7 @@ is a resting state experiment.
 
    :class:`Cattan2019_PHMD`,12,16,2,10,60s,512Hz,1
    :class:`Hinss2021`,15,62,4,1,2s,250Hz,1
+   :class:`Rodrigues2017`,20,16,2,5,10s,512Hz,1
 
 Compound Datasets
 ======================

docs/source/datasets.rst

@@ -101,6 +101,7 @@ Resting State Datasets
 
     Cattan2019_PHMD
     Hinss2021
+    Rodrigues2017
 
 
 ------------

docs/source/whats_new.rst

@@ -34,6 +34,7 @@ Enhancements
 - Add SSVEP and ERP paradigms to DL pipelines (:gh:`590` by `Pierre Guetschel`_)
 - Allow to pass a single pipeline file to ``benchmark`` (:gh:`591` by `Pierre Guetschel`_)
 - Exposing the `drop_rate` for all the deep learning parameters (:gh:`592` by `Bruno Aristimunha`_)
+- Add Alphawes dataset (:gh:`602` by `Gregoire Cattan`_ and `Pedro L. C. Rodrigues`_)
 
 Bugs
 ~~~~

moabb/datasets/__init__.py

@@ -11,6 +11,7 @@
 
 # flake8: noqa
 from .alex_mi import AlexMI
+from .alphawaves import Rodrigues2017
 from .bbci_eeg_fnirs import Shin2017A, Shin2017B
 
 # Depreciated datasets (will be removed in the future):

moabb/datasets/alphawaves.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import os
+
+import mne
+import numpy as np
+from scipy.io import loadmat
+
+from moabb.datasets import download as dl
+from moabb.datasets.base import BaseDataset
+
+
+ALPHAWAVES_URL = "https://zenodo.org/record/2348892/files/"
+
+
+class Rodrigues2017(BaseDataset):
+    """Alphawaves dataset
+
+    .. admonition:: Dataset summary
+
+
+        =============== ======= ======= ========== =============== ============ =============== ===========
+        Name            #Subj   #Chan   #Classes   #Blocks/class   Trials len   Sampling rate   #Sessions
+        =============== ======= ======= ========== =============== ============ =============== ===========
+        Rodrigues2017   20      16      2          5               10s          512Hz           1
+        =============== ======= ======= ========== =============== ============ =============== ===========
+
+
+    Dataset containing EEG recordings of subjects in a simple
+    resting-state eyes open/closed experimental protocol. Data were recorded
+    during a pilot experiment taking place in the GIPSA-lab, Grenoble,
+    France, in 2017 [1]_.
+
+    **Dataset Description**
+
+    This experiment was conducted to
+    provide a simple yet reliable set of EEG signals carrying very distinct
+    signatures on each experimental condition. It can be useful for researchers
+    and students looking for an EEG dataset to perform tests with signal
+    processing and machine learning algorithms.
+
+    I. Participants
+
+    A total of 20 volunteers participated in the experiment (7 females), with
+    mean (sd) age 25.8 (5.27) and median 25.5. 18 subjects were between 19 and
+    28 years old. Two participants with age 33 and 44 were outside this range.
+
+    II. Procedures
+
+    EEG signals were acquired using a standard research grade amplifier
+    (g.USBamp, g.tec, Schiedlberg, Austria) and the EC20 cap equipped with 16
+    wet electrodes (EasyCap, Herrsching am Ammersee, Germany), placed according
+    to the 10-20 international system.
+    We acquired the data with no digital filter and a sampling frequency of 512Hz
+    was used.
+
+    Each participant underwent one session consisting of
+    ten blocks of ten seconds of EEG data recording.
+    Five blocks were recorded while a subject was keeping his eyes
+    closed (condition 1) and the others while his eyes were open (condition 2).
+    The two conditions were alternated. Before the onset of each block, the
+    subject was asked to close or open his eyes according to the experimental
+    condition.
+
+    We supply an online and open-source example working with Python [2]_.
+
+    References
+    ----------
+
+    .. [1] G. Cattan, P. L. Coelho Rodrigues, and M. Congedo,
+           ‘EEG Alpha Waves Dataset’, 2018.
+           Available: https://hal.archives-ouvertes.fr/hal-02086581
+
+    .. [2] Rodrigues PLC. Alpha-Waves-Dataset [Internet].
+           Grenoble: GIPSA-lab; 2018. Available from:
+           https://github.com/plcrodrigues/Alpha-Waves-Dataset
+
+    Notes
+    -----
+
+    .. versionadded:: 1.0.1
+
+    """
+
+    def __init__(self):
+        subject_list = list(range(1, 6 + 1)) + list(range(8, 20 + 1))
+        super().__init__(
+            subjects=subject_list,
+            sessions_per_subject=1,
+            events=dict(closed=1, open=2),
+            code="Rodrigues2017",
+            interval=[0, 10],
+            paradigm="rstate",
+            doi="https://doi.org/10.5281/zenodo.2348892",
+        )
+
+    def _get_single_subject_data(self, subject):
+        """return data for a single subject"""
+
+        dirpath = self.data_path(subject)[0]
+        filepath = os.listdir(dirpath)[0]
+
+        data = loadmat(os.path.join(dirpath, filepath))
+
+        S = data["SIGNAL"][:, 1:17]
+        stim_close = data["SIGNAL"][:, 17]
+        stim_open = data["SIGNAL"][:, 18]
+        stim = 1 * stim_close + 2 * stim_open
+        chnames = [
+            "Fp1",
+            "Fp2",
+            "Fc5",
+            "Fz",
+            "Fc6",
+            "T7",
+            "Cz",
+            "T8",
+            "P7",
+            "P3",
+            "Pz",
+            "P4",
+            "P8",
+            "O1",
+            "Oz",
+            "O2",
+            "stim",
+        ]
+        chtypes = ["eeg"] * 16 + ["stim"]
+        X = np.concatenate([S, stim[:, None]], axis=1).T
+
+        info = mne.create_info(
+            ch_names=chnames, sfreq=512, ch_types=chtypes, verbose=False
+        )
+        raw = mne.io.RawArray(data=X, info=info, verbose=False)
+
+        return {"0": {"0": raw}}
+
+    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"))
+
+        url = "{:s}subject_{:02d}.mat".format(ALPHAWAVES_URL, subject)
+        file_path = dl.data_path(url, "ALPHAWAVES")
+
+        return [file_path]