Add c-VEP paradigm and first dataset

docs/source/dataset_summary.rst

@@ -83,6 +83,17 @@ SSVEP
    :class:`Wang2016`,34,62,40,6,5s,250Hz,1
 
 
+c-VEP
+======================
+
+
+.. csv-table::
+   :header: Dataset, #Subj, #Chan, #Classes, #Trials / class, Trials length, #Epochs / class, Sampling rate, #Sessions, Codes, Presentation rate
+   :class: sortable
+
+   :class:`Thielen2021`,30,8,20,5,31.5s,18900 NT / 18900 T,512Hz,1,Gold codes,60Hz
+
+
 Resting States
 ======================
 

docs/source/datasets.rst

@@ -75,6 +75,17 @@ SSVEP Datasets
     Lee2019_SSVEP
 
 
+--------------
+c-VEP Datasets
+--------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    Thielen2021
+
+
 ----------------------
 Resting State Datasets
 ----------------------

docs/source/paradigms.rst

@@ -42,6 +42,19 @@ SSVEP Paradigms
     SSVEP
     FilterBankSSVEP
 
+
+---------------
+c-VEP Paradigms
+---------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    CVEP
+    FilterBankCVEP
+
+
 --------------
 Fixed Interval Windows Processings
 --------------

docs/source/whats_new.rst

@@ -44,6 +44,7 @@ Enhancements
 - Rename many dataset class names to standardize and deprecate old names (:gh:`455` by `Pierre Guetschel`_)
 - Change many dataset codes to match the class names (:gh:`455` by `Pierre Guetschel`_)
 - Add :obj:`moabb.datasets.compound_dataset.utils.compound_dataset_list`  (:gh:`455` by `Pierre Guetschel`_)
+- Add c-VEP paradigm and Thielen2021 c-VEP dataset (:gh:`463` by `Jordy Thielen`_)
 
 Bugs
 ~~~~
@@ -376,3 +377,4 @@ API changes
 .. _Pierre Guetschel: https://github.com/PierreGtch
 .. _Ludovic Darmet: https://github.com/ludovicdmt
 .. _Thomas Moreau: https://github.com/tommoral
+.. _Jordy Thielen: https://github.com/thijor

moabb/datasets/__init__.py

@@ -45,6 +45,7 @@
 from .ssvep_mamem import MAMEM1, MAMEM2, MAMEM3
 from .ssvep_nakanishi import Nakanishi2015
 from .ssvep_wang import Wang2016
+from .thielen2021 import Thielen2021
 from .upper_limb import Ofner2017
 from .utils import _init_dataset_list
 from .Weibo2014 import Weibo2014

moabb/datasets/fake.py

@@ -1,3 +1,4 @@
+import re
 import tempfile
 from pathlib import Path
 
@@ -49,7 +50,7 @@ def __init__(
         self.channels = channels
         code = (
             f"{code}-{paradigm.lower()}-{n_subjects}-{n_sessions}-{n_runs}-"
-            f"{''.join([e.replace('_', '').lower() for e in event_list])}-"
+            f"{''.join([re.sub('[^A-Za-z0-9]', '', e).lower() for e in event_list])}-"
             f"{''.join([c.lower() for c in channels])}"
         )
         super().__init__(

moabb/datasets/thielen2021.py

@@ -0,0 +1,222 @@
+import h5py
+import mne
+import numpy as np
+from mne import create_info
+from mne.io import RawArray
+from scipy.io import loadmat
+
+from moabb.datasets import download as dl
+from moabb.datasets.base import BaseDataset
+
+
+Thielen2021_URL = "https://public.data.donders.ru.nl/dcc/DSC_2018.00122_448_v3"
+ELECTRODE_MAPPING = {
+    "AF3": "Fz",
+    "F3": "T7",
+    "FC5": "O1",
+    "P7": "POz",
+    "P8": "Oz",
+    "FC6": "Iz",
+    "F4": "O2",
+    "AF4": "T8",
+}
+SESSIONS = (
+    "20181128",
+    "20181206",
+    "20181217",
+    "20181217",
+    "20181217",
+    "20181218",
+    "20181218",
+    "20181219",
+    "20181219",
+    "20181220",
+    "20181220",
+    "20181220",
+    "20190107",
+    "20190107",
+    "20190110",
+    "20190110",
+    "20190110",
+    "20190117",
+    "20190117",
+    "20190118",
+    "20190118",
+    "20190118",
+    "20190220",
+    "20190222",
+    "20190225",
+    "20190301",
+    "20190307",
+    "20190308",
+    "20190311",
+    "20190311",
+)
+NR_BLOCKS = 5  # Each session consisted of 5 blocks (i.e., runs)
+NR_CYCLES_PER_TRIAL = 15  # Each trial contained 15 cycles of a 2.1 second code
+PRESENTATION_RATE = 60  # Codes were presented at a 60 Hz monitor refresh rate
+
+
+class Thielen2021(BaseDataset):
+    """c-VEP dataset from Thielen et al. (2021)
+
+    Dataset [1]_ from the study on zero-training c-VEP [2]_.
+
+    .. admonition:: Dataset summary
+
+        =============  =======  =======  ==================  ===============  ===============  ===========
+        Name             #Subj    #Chan     #Trials / class  Trials length    Sampling rate      #Sessions
+        =============  =======  =======  ==================  ===============  ===============  ===========
+        Thielen2021         30        8  18900 NT / 18900 T  0.3s             512Hz                      1
+        =============  =======  =======  ==================  ===============  ===============  ===========
+
+    **Dataset description**
+
+    EEG recordings were acquired at a sampling rate of 512 Hz, employing 8 Ag/AgCl electrodes. The Biosemi ActiveTwo EEG
+    amplifier was utilized during the experiment. The electrode array consisted of Fz, T7, O1, POz, Oz, Iz, O2, and T8,
+    connected as EXG channels.
+
+    During the experimental sessions, participants engaged in passive operation (i.e., without feedback) of a 4 x 5
+    visual speller Brain-Computer Interface (BCI), comprising 20 distinct classes. Each cell of the symbol grid
+    underwent luminance modulation at full contrast, accomplished through pseudo-random noise-codes derived from a
+    collection of modulated Gold codes. These codes are binary, have a balanced distribution of ones and zeros, and
+    adhering to a limited run-length pattern (maximum run-length of 2 bits). The codes were presented at a presentation
+    rate of 60 Hz. As one cycle of these modulated Gold codes contains 126 bits, the duration of one cycle is 2.1
+    seconds.
+
+    For each of the five blocks, a trial started with a cueing phase, during which the target symbol was highlighted in
+    a green hue for a duration of 1 second. Following this, participants maintained their gaze fixated on the target
+    symbol while all symbols flashed in accordance with their respective pseudo-random noise-codes for a duration of
+    31.5 seconds (i.e., 15 code cycles). Each block encompassed 20 trials, presented in a randomized sequence, thereby
+    ensuring that each symbol was attended to once within the span of a block.
+
+    Note, here, we only load the offline data of this study, and ignore the online phase.
+
+    References
+    ----------
+
+    .. [1] Thielen, J. (Jordy), Pieter Marsman, Jason Farquhar, Desain, P.W.M. (Peter) (2023): From full calibration to
+           zero training for a code-modulated visual evoked potentials brain computer interface. Version 3. Radboud
+           University. (dataset). DOI: https://doi.org/10.34973/9txv-z787
+
+    .. [2] Thielen, J., Marsman, P., Farquhar, J., & Desain, P. (2021). From full calibration to zero training for a
+           code-modulated visual evoked potentials for brain–computer interface. Journal of Neural Engineering, 18(5),
+           056007. DOI: https://doi.org/10.1088/1741-2552/abecef
+
+    Notes
+    -----
+
+    .. versionadded:: 0.6.0
+
+    """
+
+    def __init__(self):
+        super().__init__(
+            subjects=list(range(1, 30 + 1)),
+            sessions_per_subject=1,
+            events={"1.0": 101, "0.0": 100},
+            code="Thielen2021",
+            interval=(0, 0.3),
+            paradigm="cvep",
+            doi="10.34973/9txv-z787",
+        )
+
+    def _get_single_subject_data(self, subject):
+        """Return the data of a single subject."""
+        file_path_list = self.data_path(subject)
+
+        # Codes
+        codes = np.tile(loadmat(file_path_list[-1])["codes"], (NR_CYCLES_PER_TRIAL, 1))
+
+        # There is only one session, each of 5 blocks (i.e., runs)
+        sessions = {"session_1": {}}
+        for i_b in range(NR_BLOCKS):
+            # EEG
+            raw = mne.io.read_raw_gdf(
+                file_path_list[2 * i_b],
+                stim_channel="status",
+                preload=True,
+                verbose=False,
+            )
+            mne.rename_channels(raw.info, ELECTRODE_MAPPING)
+
+            # Labels at trial level (i.e., symbols)
+            labels = (
+                np.array(h5py.File(file_path_list[2 * i_b + 1], "r")["v"])
+                .astype("uint8")
+                .flatten()
+                - 1
+            )
+
+            # Find onsets of trials
+            # N.B. Every 2.1 seconds an event was generated (15 times per trial, plus one 16th "leaking epoch")
+            # N.B. This "leaking epoch" is not always present, so taking epoch[::16, :] won't work
+            events = mne.find_events(raw, verbose=False)
+            cond = np.logical_or(
+                np.diff(events[:, 0]) < 1.8 * raw.info["sfreq"],
+                np.diff(events[:, 0]) > 2.4 * raw.info["sfreq"],
+            )
+            idx = np.concatenate(([0], 1 + np.where(cond)[0]))
+            onsets = events[idx, 0]
+
+            # Create stim channel with trial information (i.e., symbols)
+            # N.B. 200 = symbol-0, 201 = symbol-1, 202 = symbol-2, etc.
+            stim_chan = np.zeros((1, len(raw)))
+            for onset, label in zip(onsets, labels):
+                stim_chan[0, onset] = 200 + label
+            info = create_info(
+                ch_names=["stim_trial"],
+                ch_types=["stim"],
+                sfreq=raw.info["sfreq"],
+                verbose=False,
+            )
+            raw = raw.add_channels([RawArray(data=stim_chan, info=info, verbose=False)])
+
+            # Create stim channel with epoch information (i.e., 1 / 0, or on / off)
+            # N.B. 100 = "0", 101 = "1"
+            stim_chan = np.zeros((1, len(raw)))
+            for onset, label in zip(onsets, labels):
+                idx = (
+                    onset
+                    + np.arange(codes.shape[0]) / PRESENTATION_RATE * raw.info["sfreq"]
+                ).astype("int")
+                stim_chan[0, idx] = 100 + codes[:, label]
+            info = create_info(
+                ch_names=["stim_epoch"],
+                ch_types=["stim"],
+                sfreq=raw.info["sfreq"],
+                verbose=False,
+            )
+            raw = raw.add_channels([RawArray(data=stim_chan, info=info, verbose=False)])
+
+            # Add data as a new run
+            sessions["session_1"][f"run_{1 + i_b:02d}"] = raw
+
+        return sessions
+
+    def data_path(
+        self, subject, path=None, force_update=False, update_path=None, verbose=None
+    ):
+        """Return the data paths of a single subject."""
+        if subject not in self.subject_list:
+            raise (ValueError("Invalid subject number"))
+
+        sub = f"sub-{subject:02d}"
+        ses = SESSIONS[subject - 1]
+        subject_paths = []
+        for i_b in range(NR_BLOCKS):
+            blk = f"block_{1 + i_b:d}"
+
+            # EEG
+            url = f"{Thielen2021_URL:s}/sourcedata/offline/{sub}/{blk}/{sub}_{ses}_{blk}_main_eeg.gdf"
+            subject_paths.append(dl.data_dl(url, self.code, path, force_update, verbose))
+
+            # Labels at trial level (i.e., symbols)
+            url = f"{Thielen2021_URL:s}/sourcedata/offline/{sub}/{blk}/trainlabels.mat"
+            subject_paths.append(dl.data_dl(url, self.code, path, force_update, verbose))
+
+        # Codes
+        url = f"{Thielen2021_URL:s}/resources/mgold_61_6521_flip_balanced_20.mat"
+        subject_paths.append(dl.data_dl(url, self.code, path, force_update, verbose))
+
+        return subject_paths

moabb/paradigms/__init__.py

@@ -5,10 +5,10 @@
 have two-class, multi-class, or continuous paradigms; similarly,
 different preprocessing is necessary for ERP vs ERD paradigms.
 """
+# flake8: noqa
+from moabb.paradigms.cvep import *
 from moabb.paradigms.fixed_interval_windows import *
 from moabb.paradigms.motor_imagery import *
-
-# flake8: noqa
 from moabb.paradigms.p300 import *
 from moabb.paradigms.resting_state import *
 from moabb.paradigms.ssvep import *