def test_filterbank(base_concat_ds):
base_concat_ds = base_concat_ds.split([[0]])['0']
preprocessors = [
Preprocessor('pick_channels',
ch_names=sorted(['C4', 'Cz']),
ordered=True),
Preprocessor(filterbank,
frequency_bands=[(0, 4), (4, 8), (8, 13)],
drop_original_signals=False,
apply_on_array=False)
]
preprocess(base_concat_ds, preprocessors)
for x, y in base_concat_ds:
break
assert x.shape[0] == 8
freq_band_annots = [
ch.split('_')[-1] for ch in base_concat_ds.datasets[0].raw.ch_names
if '_' in ch
]
assert len(np.unique(freq_band_annots)) == 3
np.testing.assert_array_equal(base_concat_ds.datasets[0].raw.ch_names, [
'C4',
'C4_0-4',
'C4_4-8',
'C4_8-13',
'Cz',
'Cz_0-4',
'Cz_4-8',
'Cz_8-13',
])
def test_scale_continuous(base_concat_ds):
factor = 1e6
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=False),
Preprocessor(scale, factor=factor)
]
raw_timepoint = base_concat_ds[0][0][:22] # only keep EEG channels
preprocess(base_concat_ds, preprocessors)
np.testing.assert_allclose(base_concat_ds[0][0],
raw_timepoint * factor,
rtol=1e-4,
atol=1e-4)
def test_scale_windows(windows_concat_ds):
factor = 1e6
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=False),
Preprocessor(scale, factor=factor)
]
raw_window = windows_concat_ds[0][0][:22] # only keep EEG channels
preprocess(windows_concat_ds, preprocessors)
np.testing.assert_allclose(windows_concat_ds[0][0],
raw_window * factor,
rtol=1e-4,
atol=1e-4)
def test_windows_fixed_length_cropped(lazy_loadable_dataset):
"""Test fixed length windowing on cropped data.
Cropping raw data changes the `first_samp` attribute of the Raw object, and
so it is important to test this is taken into account by the windowers.
"""
tmin, tmax = 100, 120
ds = copy.deepcopy(lazy_loadable_dataset)
ds.datasets[0].raw.annotations.crop(tmin, tmax)
crop_ds = copy.deepcopy(lazy_loadable_dataset)
crop_transform = Preprocessor('crop', tmin=tmin, tmax=tmax)
preprocess(crop_ds, [crop_transform])
# Extract windows
sfreq = ds.datasets[0].raw.info['sfreq']
tmin_samples, tmax_samples = int(tmin * sfreq), int(tmax * sfreq)
windows1 = create_fixed_length_windows(
concat_ds=ds, start_offset_samples=tmin_samples,
stop_offset_samples=tmax_samples, window_size_samples=100,
window_stride_samples=100, drop_last_window=True)
windows2 = create_fixed_length_windows(
concat_ds=crop_ds, start_offset_samples=0,
stop_offset_samples=None, window_size_samples=100,
window_stride_samples=100, drop_last_window=True)
assert (windows1[0][0] == windows2[0][0]).all()
def test_filterbank_order_channels_by_freq(base_concat_ds):
base_concat_ds = base_concat_ds.split([[0]])['0']
preprocessors = [
Preprocessor('pick_channels',
ch_names=sorted(['C4', 'Cz']),
ordered=True),
Preprocessor(filterbank,
frequency_bands=[(0, 4), (4, 8), (8, 13)],
drop_original_signals=False,
order_by_frequency_band=True,
apply_on_array=False)
]
preprocess(base_concat_ds, preprocessors)
np.testing.assert_array_equal(base_concat_ds.datasets[0].raw.ch_names, [
'C4', 'Cz', 'C4_0-4', 'Cz_0-4', 'C4_4-8', 'Cz_4-8', 'C4_8-13',
'Cz_8-13'
])
def test_zscore_windows(windows_concat_ds):
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=False),
Preprocessor(zscore)
]
preprocess(windows_concat_ds, preprocessors)
for ds in windows_concat_ds.datasets:
windowed_data = ds.windows.get_data()
shape = windowed_data.shape
# zero mean
expected = np.zeros(shape[:-1])
np.testing.assert_allclose(windowed_data.mean(axis=-1),
expected,
rtol=1e-4,
atol=1e-4)
# unit variance
expected = np.ones(shape[:-1])
np.testing.assert_allclose(windowed_data.std(axis=-1),
expected,
rtol=1e-4,
atol=1e-4)
def test_zscore_continuous(base_concat_ds):
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=False),
Preprocessor(zscore, channel_wise=True)
]
preprocess(base_concat_ds, preprocessors)
for ds in base_concat_ds.datasets:
raw_data = ds.raw.get_data()
shape = raw_data.shape
# zero mean
expected = np.zeros(shape[:-1])
np.testing.assert_allclose(raw_data.mean(axis=-1),
expected,
rtol=1e-4,
atol=1e-4)
# unit variance
expected = np.ones(shape[:-1])
np.testing.assert_allclose(raw_data.std(axis=-1),
expected,
rtol=1e-4,
atol=1e-4)
def test_preprocess_windows_callable_on_object(windows_concat_ds):
factor = 10
preprocessors = [
Preprocessor(modify_windows_object,
apply_on_array=False,
factor=factor)
]
raw_window = windows_concat_ds[0][0]
preprocess(windows_concat_ds, preprocessors)
np.testing.assert_allclose(windows_concat_ds[0][0],
raw_window * factor,
rtol=1e-4,
atol=1e-4)
def test_windows_from_events_cropped(lazy_loadable_dataset):
"""Test windowing from events on cropped data.
Cropping raw data changes the `first_samp` attribute of the Raw object, and
so it is important to test this is taken into account by the windowers.
"""
tmin, tmax = 100, 120
ds = copy.deepcopy(lazy_loadable_dataset)
ds.datasets[0].raw.annotations.crop(tmin, tmax)
crop_ds = copy.deepcopy(lazy_loadable_dataset)
crop_transform = Preprocessor('crop', tmin=tmin, tmax=tmax)
preprocess(crop_ds, [crop_transform])
# Extract windows
windows1 = create_windows_from_events(
concat_ds=ds, trial_start_offset_samples=0, trial_stop_offset_samples=0,
window_size_samples=100, window_stride_samples=100,
drop_last_window=False)
windows2 = create_windows_from_events(
concat_ds=crop_ds, trial_start_offset_samples=0,
trial_stop_offset_samples=0, window_size_samples=100,
window_stride_samples=100, drop_last_window=False)
assert (windows1[0][0] == windows2[0][0]).all()
# Make sure events that fall outside of recording will trigger an error
with pytest.raises(
ValueError, match='"trial_stop_offset_samples" too large'):
create_windows_from_events(
concat_ds=ds, trial_start_offset_samples=0,
trial_stop_offset_samples=10000, window_size_samples=100,
window_stride_samples=100, drop_last_window=False)
with pytest.raises(
ValueError, match='"trial_stop_offset_samples" too large'):
create_windows_from_events(
concat_ds=crop_ds, trial_start_offset_samples=0,
trial_stop_offset_samples=2001, window_size_samples=100,
window_stride_samples=100, drop_last_window=False)
# ds has a pandas DataFrame with additional description of its internal datasets
display(ds.description)
##############################################################################
# We can iterate through ds which yields one time point of a continuous signal x,
# and a target y (which can be None if targets are not defined for the entire
# continuous signal).
for x, y in ds:
print(x.shape, y)
break
##############################################################################
# We can apply preprocessing transforms that are defined in mne and work
# in-place, such as resampling, bandpass filtering, or electrode selection.
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=True),
Preprocessor('resample', sfreq=100)
]
print(ds.datasets[0].raw.info["sfreq"])
preprocess(ds, preprocessors)
print(ds.datasets[0].raw.info["sfreq"])
###############################################################################
# We can easily split ds based on a criteria applied to the description
# DataFrame:
subsets = ds.split("session")
print({subset_name: len(subset) for subset_name, subset in subsets.items()})
###############################################################################
# Next, we use a windower to extract events from the dataset based on events:
windows_ds = create_windows_from_events(
def train(subject_id):
print('\n--------------------------------------------------\n')
print(
'Training on BCI_IV_2a dataset | Cross-subject | ID: {:02d}\n'.format(
subject_id))
##### subject_range = [subject_id]
subject_range = [x for x in range(1, 10)]
dataset = MOABBDataset(dataset_name="BNCI2014001",
subject_ids=subject_range)
######################################################################
# Preprocessing
low_cut_hz = 4. # low cut frequency for filtering
high_cut_hz = 38. # high cut frequency for filtering
# Parameters for exponential moving standardization
factor_new = 1e-3
init_block_size = 1000
preprocessors = [
Preprocessor('pick_types', eeg=True, eog=False, meg=False,
stim=False), # Keep EEG sensors
Preprocessor(lambda x: x * 1e6), # Convert from V to uV
Preprocessor('filter', l_freq=low_cut_hz,
h_freq=high_cut_hz), # Bandpass filter
#Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),
Preprocessor('resample', sfreq=125),
Preprocessor(covariance_align),
## Preprocessor(exponential_moving_standardize, # Exponential moving standardization
## factor_new=factor_new, init_block_size=init_block_size)
## Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),
]
# Transform the data
print('Preprocessing dataset\n')
preprocess(dataset, preprocessors)
######################################################################
# Cut Compute Windows
# ~~~~~~~~~~~~~~~~~~~
trial_start_offset_seconds = -0.5
trial_stop_offset_seconds = 0.0
# Extract sampling frequency, check that they are same in all datasets
sfreq = dataset.datasets[0].raw.info['sfreq']
assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
# Calculate the trial start offset in samples.
trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
trial_stop_offset_samples = int(trial_stop_offset_seconds * sfreq)
# Create windows using braindecode function for this. It needs parameters to define how
# trials should be used.
print('Windowing dataset\n')
windows_dataset = create_windows_from_events(
dataset,
# picks=["Fz", "FC3", "FC1", "FCz", "FC2", "FC4", "C5", "C3", "C1", "Cz", "C2", "C4", "C6", "CP3", "CP1", "CPz", "CP2", "CP4", "P1", "Pz", "P2", "POz"],
trial_start_offset_samples=trial_start_offset_samples,
trial_stop_offset_samples=trial_stop_offset_samples,
preload=True,
)
print('Computing covariances of each WindowsDataset')
windows_dataset.compute_covariances_concat()
# print(windows_dataset.datasets[0].windows)
######################################################################
# Merge multiple datasets into a single WindowDataset
# metadata_all = [ds.windows.metadata for ds in windows_dataset.datasets]
# metadata_full = pd.concat(metadata_all)
"""
epochs_all = [ds.windows for ds in windows_dataset.datasets]
epochs_full = mne.concatenate_epochs(epochs_all)
full_dataset = WindowsDataset(windows=epochs_full, description=None, transform=None)
windows_dataset = full_dataset
"""
######################################################################
# Split dataset into train and valid
# keep only session 1:
# temp = windows_dataset.split( 'session' )
# windows_dataset = temp['session_T']
# print(windows_dataset.datasets[0].windows)
# print(windows_dataset.datasets[0].windows.get_data().shape)
# quit()
subject_column = windows_dataset.description['subject'].values
inds_train = list(np.where(subject_column != subject_id)[0])
inds_valid = list(np.where(subject_column == subject_id)[0])
splitted = windows_dataset.split([inds_train, inds_valid])
train_set = splitted['0']
valid_set = splitted['1']
#######
epochs_all = [ds.windows for ds in train_set.datasets]
epochs_full = mne.concatenate_epochs(epochs_all)
trialwise_weights_all = [ds.trialwise_weights for ds in train_set.datasets]
trialwise_weights_full = np.hstack(trialwise_weights_all)
full_dataset = WindowsDataset(windows=epochs_full,
description=None,
transform=None)
full_dataset.trialwise_weights = trialwise_weights_full
train_set = full_dataset
# print(train_set.windows.metadata)
######################################################################
# Create model
cuda = torch.cuda.is_available(
) # check if GPU is available, if True chooses to use it
device = 'cuda' if cuda else 'cpu'
if cuda:
torch.backends.cudnn.benchmark = True
seed = 20200220 # random seed to make results reproducible
# Set random seed to be able to reproduce results
set_random_seeds(seed=seed, cuda=cuda)
n_classes = 4
# Extract number of chans and time steps from dataset
n_chans = train_set[0][0].shape[0]
input_window_samples = train_set[0][0].shape[1]
"""
model = ShallowFBCSPNet(
n_chans,
n_classes,
input_window_samples=input_window_samples,
final_conv_length='auto')
"""
"""
model = EEGNetv1(
n_chans,
n_classes,
input_window_samples=input_window_samples,
final_conv_length="auto",
pool_mode="mean",
second_kernel_size=(2, 32),
third_kernel_size=(8, 4),
drop_prob=0.25)
"""
"""
model = HybridNet(n_chans, n_classes,
input_window_samples=input_window_samples)
"""
"""
model = TCN(n_chans, n_classes,
n_blocks=6,
n_filters=32,
kernel_size=9,
drop_prob=0.0,
add_log_softmax=True)
"""
model = EEGNetv4(
n_chans,
n_classes,
input_window_samples=input_window_samples,
final_conv_length="auto",
pool_mode="mean",
F1=8,
D=2,
F2=16, # usually set to F1*D (?)
kernel_length=64,
third_kernel_size=(8, 4),
drop_prob=0.2)
if cuda:
model.cuda()
######################################################################
# Training
# These values we found good for shallow network:
lr = 0.01 # 0.0625 * 0.01
weight_decay = 0.0005
# For deep4 they should be:
# lr = 1 * 0.01
# weight_decay = 0.5 * 0.001
batch_size = 64
n_epochs = 100
# clf = EEGClassifier(
clf = EEGClassifier_weighted(
model,
criterion=torch.nn.NLLLoss,
optimizer=torch.optim.SGD, #AdamW,
train_split=predefined_split(
valid_set), # using valid_set for validation
optimizer__lr=lr,
optimizer__momentum=0.9,
optimizer__weight_decay=weight_decay,
batch_size=batch_size,
callbacks=[
"accuracy", #("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
],
device=device,
)
# Model training for a specified number of epochs. `y` is None as it is already supplied
# in the dataset.
clf.fit(train_set, y=None, epochs=n_epochs)
results_columns = [
'train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy'
]
df = pd.DataFrame(clf.history[:, results_columns],
columns=results_columns,
index=clf.history[:, 'epoch'])
val_accs = df['valid_accuracy'].values
max_val_acc = 100.0 * np.max(val_accs)
return max_val_acc
def test_preprocess_windows_str(windows_concat_ds):
preprocessors = [
Preprocessor('crop', tmin=0, tmax=0.1, include_tmax=False)
]
preprocess(windows_concat_ds, preprocessors)
assert windows_concat_ds[0][0].shape[1] == 25
def test_preprocess_raw_str(base_concat_ds):
preprocessors = [Preprocessor('crop', tmax=10, include_tmax=False)]
preprocess(base_concat_ds, preprocessors)
assert len(base_concat_ds.datasets[0].raw.times) == 2500
def test_method_not_available(base_concat_ds):
preprocessors = [Preprocessor('this_method_is_not_real', )]
with pytest.raises(AttributeError):
preprocess(base_concat_ds, preprocessors)
# These prepocessings are now directly applied to the loaded
# data, and not on-the-fly applied as transformations in
# PyTorch-libraries like
# `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
#
from braindecode.datautil.preprocess import (exponential_moving_standardize, preprocess, Preprocessor)
low_cut_hz = 4. # low cut frequency for filtering
high_cut_hz = 38. # high cut frequency for filtering
# Parameters for exponential moving standardization
factor_new = 1e-3
init_block_size = 1000
preprocessors = [
Preprocessor('pick_types', eeg=True, meg=False, stim=False), # Keep EEG sensors
Preprocessor(lambda x: x * 1e6), # Convert from V to uV
Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz), # Bandpass filter
Preprocessor(exponential_moving_standardize, # Exponential moving standardization
factor_new=factor_new, init_block_size=init_block_size)
]
# Transform the data
preprocess(dataset, preprocessors) # in place modification
######################################################################
# Cut Compute Windows
# ~~~~~~~~~~~~~~~~~~~
#
# ~~~~~~~~~~~~~
#
######################################################################
# Next, we preprocess the raw data. We apply convert the data to microvolts and
# apply a lowpass filter. We omit the downsampling step of [1]_ as the Sleep
# Physionet data is already sampled at a lower 100 Hz.
#
from braindecode.datautil.preprocess import preprocess, Preprocessor
high_cut_hz = 30
preprocessors = [
Preprocessor(lambda x: x * 1e6),
Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
]
# Transform the data
preprocess(dataset, preprocessors)
######################################################################
# Extract windows
# ~~~~~~~~~~~~~~~
#
######################################################################
# We extract 30-s windows to be used in the classification task.
def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
# crop recordings to tmin – tmax. can be incomplete if recording
# has lower duration than tmax
# by default mne fails if tmax is bigger than duration
tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)
raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)
tmin = 1 * 60
tmax = 6 * 60
sfreq = 100
preprocessors = [
Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,
apply_on_array=False),
Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),
Preprocessor(custom_rename_channels, mapping=ch_mapping,
apply_on_array=False),
Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),
Preprocessor(lambda x: x * 1e6),
Preprocessor('resample', sfreq=sfreq),
]
###############################################################################
# The preprocessing loop works as follows. For every recording, we apply the
# preprocessors as defined above. Then, we update the description of the rec,
# since we have altered the duration, the reference, and the sampling frequency.
# Afterwards, we split the continuous signals into compute windows. We store
# each recording to a unique subdirectory that is named corresponding to the