Merge pull request #1 from NeuroTechX/master

Update from original

.github/workflows/test.yml

@@ -0,0 +1,38 @@
+name: Test
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  test:
+    name: ${{ matrix.os }}, py-${{ matrix.python_version }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-18.04, windows-latest, macOS-latest]
+        python_version: [3.6]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python_version }}
+    - name: Install dependencies
+      shell: bash
+      run: |
+        python -m pip install --upgrade pip wheel flake8
+        pip install -r requirements.txt
+        pip install .
+    - name: Run tests
+      shell: bash
+      run: |
+        python -m unittest moabb.tests
+        python -m moabb.run --pipelines=./moabb/tests/test_pipelines/ --verbose
+    - name: Run linting
+      shell: bash
+      run: |
+        flake8 moabb

LICENSE

@@ -1,24 +1,29 @@
-Copyright © 2017, authors of moabb
+BSD 3-Clause License
+
+Copyright (c) 2017, authors of moabb
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
-    * Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-    * Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-    * Neither the names of moabb authors nor the names of any
-      contributors may be used to endorse or promote products derived from
-      this software without specific prior written permission.
 
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY
-DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

@@ -10,7 +10,7 @@
 
 **This is work in progress. API will change significantly (as well as the results of the benchmark).**
 
-[![Build Status](https://travis-ci.org/NeuroTechX/moabb.svg?branch=master)](https://travis-ci.org/NeuroTechX/moabb)
+[![Build Status](https://github.com/NeuroTechX/moabb/workflows/Test/badge.svg)](https://github.com/NeuroTechX/moabb/actions?query=branch%3Amaster)
 
 ## Welcome!
 

moabb/datasets/epfl.py

@@ -116,6 +116,13 @@ def _get_single_run_data(self, file_path):
             'MA2']
         ch_types = ['eeg'] * 32 + ['misc'] * 2
 
+        # The last X entries are 0 for all signals. This leads to
+        # artifacts when epoching and band-pass filtering the data.
+        # Correct the signals for this.
+        sig_i = np.where(
+            np.diff(np.all(signals == 0, axis=0).astype(int)) != 0)[0][0]
+        signals = signals[:, :sig_i]
+        signals *= 1e-6  # data is stored as uV, but MNE expects V
         # we have to re-reference the signals
         # the average signal on the mastoids electrodes is used as reference
         references = [32, 33]

moabb/datasets/schirrmeister2017.py

@@ -198,8 +198,9 @@ def get_all_sensors(filename, pattern=None):
         """
         with h5py.File(filename, 'r') as h5file:
             clab_set = h5file['nfo']['clab'][:].squeeze()
-            all_sensor_names = [''.join(chr(c) for c in h5file[obj_ref]) for
-                                obj_ref in clab_set]
+            all_sensor_names = [''.join(
+                chr(c.squeeze()) for c in h5file[obj_ref])
+                                for obj_ref in clab_set]
             if pattern is not None:
                 all_sensor_names = filter(
                     lambda sname: re.search(pattern, sname),

moabb/datasets/upper_limb.py

@@ -1,6 +1,6 @@
 from moabb.datasets.base import BaseDataset
 
-from mne.io import read_raw_edf
+from mne.io import read_raw_gdf
 from mne.channels import make_standard_montage
 import numpy as np
 
@@ -94,7 +94,7 @@ def _get_single_subject_data(self, subject):
             montage = make_standard_montage('standard_1005')
             data = {}
             for ii, path in enumerate(paths):
-                raw = read_raw_edf(path, eog=eog, misc=range(64, 96),
+                raw = read_raw_gdf(path, eog=eog, misc=range(64, 96),
                                    preload=True, verbose='ERROR')
                 raw.set_montage(montage)
                 # there is nan in the data

moabb/paradigms/base.py

@@ -75,14 +75,11 @@ def process_raw(self, raw, dataset, return_epochs=False):
 
         Parameters
         ----------
-
         raw: mne.Raw instance
             the raw EEG data.
-
         dataset : dataset instance
             The dataset corresponding to the raw file. mainly use to access
             dataset specific information.
-
         return_epochs: boolean
             This flag specifies whether to return only the data array or the
             complete processed mne.Epochs
@@ -93,10 +90,8 @@ def process_raw(self, raw, dataset, return_epochs=False):
             the data that will be used as features for the model
             Note: if return_epochs=True,  this is mne.Epochs
                   if return_epochs=False, this is np.ndarray
-
         labels: np.ndarray
             the labels for training / evaluating the model
-
         metadata: pd.DataFrame
             A dataframe containing the metadata
 
@@ -105,18 +100,23 @@ def process_raw(self, raw, dataset, return_epochs=False):
         event_id = self.used_events(dataset)
 
         # find the events, first check stim_channels then annotations
-        stim_channels = mne.utils._get_stim_channel(
-            None, raw.info, raise_error=False)
+        stim_channels = mne.utils._get_stim_channel(None, raw.info,
+                                                    raise_error=False)
         if len(stim_channels) > 0:
             events = mne.find_events(raw, shortest_event=0, verbose=False)
         else:
-            events, _ = mne.events_from_annotations(raw, event_id=event_id,
-                                                    verbose=False)
-        channels = () if self.channels is None else self.channels
+            try:
+                events, _ = mne.events_from_annotations(raw,
+                                                        event_id=event_id,
+                                                        verbose=False)
+            except ValueError:
+                events, _ = mne.events_from_annotations(raw, verbose=False)
 
         # picks channels
-        picks = mne.pick_types(raw.info, eeg=True, stim=False,
-                               include=channels)
+        if self.channels is None:
+            picks = mne.pick_types(raw.info, eeg=True, stim=False)
+        else:
+            picks = mne.pick_types(raw.info, stim=False, include=self.channels)
 
         # pick events, based on event_id
         try:
@@ -139,11 +139,23 @@ def process_raw(self, raw, dataset, return_epochs=False):
             raw_f = raw.copy().filter(fmin, fmax, method='iir',
                                       picks=picks, verbose=False)
             # epoch data
+            baseline = self.baseline
+            if baseline is not None:
+                baseline = (self.baseline[0] + dataset.interval[0],
+                            self.baseline[1] + dataset.interval[0])
+                bmin = baseline[0] if baseline[0] < tmin else tmin
+                bmax = baseline[1] if baseline[1] > tmax else tmax
+            else:
+                bmin = tmin
+                bmax = tmax
             epochs = mne.Epochs(raw_f, events, event_id=event_id,
-                                tmin=tmin, tmax=tmax, proj=False,
-                                baseline=None, preload=True,
+                                tmin=bmin, tmax=bmax, proj=False,
+                                baseline=baseline, preload=True,
                                 verbose=False, picks=picks,
+                                event_repeated='drop',
                                 on_missing='ignore')
+            if bmin < tmin or bmax > tmax:
+                epochs.crop(tmin=tmin, tmax=tmax)
             if self.resample is not None:
                 epochs = epochs.resample(self.resample)
             # rescale to work with uV
@@ -181,11 +193,16 @@ def get_data(self, dataset, subjects=None, return_epochs=False):
             A dataset instance.
         subjects: List of int
             List of subject number
+        return_epochs: boolean
+            This flag specifies whether to return only the data array or the
+            complete processed mne.Epochs
 
         returns
         -------
-        X : np.ndarray
+        X : Union[np.ndarray, mne.Epochs]
             the data that will be used as features for the model
+            Note: if return_epochs=True,  this is mne.Epochs
+                  if return_epochs=False, this is np.ndarray
         labels: np.ndarray
             the labels for training / evaluating the model
         metadata: pd.DataFrame

moabb/paradigms/motor_imagery.py

@@ -36,6 +36,14 @@ class BaseMotorImagery(BaseParadigm):
         5 second after the begining of the task as defined in the dataset. If
         None, use the dataset value.
 
+    baseline: None | tuple of length 2
+            The time interval to consider as “baseline” when applying baseline
+            correction. If None, do not apply baseline correction.
+            If a tuple (a, b), the interval is between a and b (in seconds),
+            including the endpoints.
+            Correction is applied by computing the mean of the baseline period
+            and subtracting it from the data (see mne.Epochs)
+
     channels: list of str | None (default None)
         list of channel to select. If None, use all EEG channels available in
         the dataset.
@@ -45,11 +53,12 @@ class BaseMotorImagery(BaseParadigm):
     """
 
     def __init__(self, filters=([7, 35],), events=None, tmin=0.0, tmax=None,
-                 channels=None, resample=None):
+                 baseline=None, channels=None, resample=None):
         super().__init__()
         self.filters = filters
-        self.channels = channels
         self.events = events
+        self.channels = channels
+        self.baseline = baseline
         self.resample = resample
 
         if (tmax is not None):
@@ -120,6 +129,14 @@ class SinglePass(BaseMotorImagery):
         5 second after the begining of the task as defined in the dataset. If
         None, use the dataset value.
 
+    baseline: None | tuple of length 2
+            The time interval to consider as “baseline” when applying baseline
+            correction. If None, do not apply baseline correction.
+            If a tuple (a, b), the interval is between a and b (in seconds),
+            including the endpoints.
+            Correction is applied by computing the mean of the baseline period
+            and subtracting it from the data (see mne.Epochs)
+
     channels: list of str | None (default None)
         list of channel to select. If None, use all EEG channels available in
         the dataset.
@@ -298,6 +315,14 @@ class MotorImagery(SinglePass):
         5 second after the begining of the task as defined in the dataset. If
         None, use the dataset value.
 
+    baseline: None | tuple of length 2
+            The time interval to consider as “baseline” when applying baseline
+            correction. If None, do not apply baseline correction.
+            If a tuple (a, b), the interval is between a and b (in seconds),
+            including the endpoints.
+            Correction is applied by computing the mean of the baseline period
+            and subtracting it from the data (see mne.Epochs)
+
     channels: list of str | None (default None)
         list of channel to select. If None, use all EEG channels available in
         the dataset.

moabb/paradigms/p300.py

@@ -39,6 +39,14 @@ class BaseP300(BaseParadigm):
         5 second after the begining of the task as defined in the dataset. If
         None, use the dataset value.
 
+    baseline: None | tuple of length 2
+            The time interval to consider as “baseline” when applying baseline
+            correction. If None, do not apply baseline correction.
+            If a tuple (a, b), the interval is between a and b (in seconds),
+            including the endpoints.
+            Correction is applied by computing the mean of the baseline period
+            and subtracting it from the data (see mne.Epochs)
+
     channels: list of str | None (default None)
         list of channel to select. If None, use all EEG channels available in
         the dataset.
@@ -48,11 +56,12 @@ class BaseP300(BaseParadigm):
     """
 
     def __init__(self, filters=([1, 24],), events=None, tmin=0.0, tmax=None,
-                 channels=None, resample=None):
+                 baseline=None, channels=None, resample=None):
         super().__init__()
         self.filters = filters
-        self.channels = channels
         self.events = events
+        self.channels = channels
+        self.baseline = baseline
         self.resample = resample
 
         if (tmax is not None):
@@ -99,6 +108,12 @@ def process_raw(self, raw, dataset, return_epochs=False):
 
         # pick events, based on event_id
         try:
+            if (type(event_id['Target']) is list and
+                    type(event_id['NonTarget']) == list):
+                event_id_new = dict(Target=1, NonTarget=0)
+                events = mne.merge_events(events, event_id['Target'], 1)
+                events = mne.merge_events(events, event_id['NonTarget'], 0)
+                event_id = event_id_new
             events = mne.pick_events(events, include=list(event_id.values()))
         except RuntimeError:
             # skip raw if no event found
@@ -118,11 +133,22 @@ def process_raw(self, raw, dataset, return_epochs=False):
             raw_f = raw.copy().filter(fmin, fmax, method='iir',
                                       picks=picks, verbose=False)
             # epoch data
+            baseline = self.baseline
+            if baseline is not None:
+                baseline = (self.baseline[0] + dataset.interval[0],
+                            self.baseline[1] + dataset.interval[0])
+                bmin = baseline[0] if baseline[0] < tmin else tmin
+                bmax = baseline[1] if baseline[1] > tmax else tmax
+            else:
+                bmin = tmin
+                bmax = tmax
             epochs = mne.Epochs(raw_f, events, event_id=event_id,
-                                tmin=tmin, tmax=tmax, proj=False,
-                                baseline=None, preload=True,
+                                tmin=bmin, tmax=bmax, proj=False,
+                                baseline=baseline, preload=True,
                                 verbose=False, picks=picks,
                                 on_missing='ignore')
+            if bmin < tmin or bmax > tmax:
+                epochs.crop(tmin=tmin, tmax=tmax)
             if self.resample is not None:
                 epochs = epochs.resample(self.resample)
             # rescale to work with uV
@@ -187,6 +213,14 @@ class SinglePass(BaseP300):
         5 second after the begining of the task as defined in the dataset. If
         None, use the dataset value.
 
+    baseline: None | tuple of length 2
+            The time interval to consider as “baseline” when applying baseline
+            correction. If None, do not apply baseline correction.
+            If a tuple (a, b), the interval is between a and b (in seconds),
+            including the endpoints.
+            Correction is applied by computing the mean of the baseline period
+            and subtracting it from the data (see mne.Epochs)
+
     channels: list of str | None (default None)
         list of channel to select. If None, use all EEG channels available in
         the dataset.