def load_train_test_hgd(subject_id):
hgd_names = [
'Fp2', 'Fp1', 'F4', 'F3', 'C4', 'C3', 'P4', 'P3', 'O2', 'O1', 'F8',
'F7', 'T8', 'T7', 'P8', 'P7', 'M2', 'M1', 'Fz', 'Cz', 'Pz'
]
log.info("Loading dataset..")
# using the moabb dataset to load our data
dataset = MOABBDataset(dataset_name="Schirrmeister2017",
subject_ids=[subject_id])
sfreq = 32
train_whole_set = dataset.split('run')['train']
log.info("Preprocessing dataset..")
# Define preprocessing steps
preprocessors = [
# convert from volt to microvolt, directly modifying the numpy array
MNEPreproc(
fn='set_eeg_reference',
ref_channels='average',
),
MNEPreproc(fn='pick_channels', ch_names=hgd_names, ordered=True),
NumpyPreproc(fn=lambda x: x * 1e6),
NumpyPreproc(fn=lambda x: np.clip(x, -800, 800)),
NumpyPreproc(fn=lambda x: x / 10),
MNEPreproc(fn='resample', sfreq=sfreq),
NumpyPreproc(fn=lambda x: np.clip(x, -80, 80)),
NumpyPreproc(fn=lambda x: x / 3),
NumpyPreproc(fn=exponential_moving_demean,
init_block_size=int(sfreq * 10),
factor_new=1 / (sfreq * 5)),
# keep only EEG sensors
# NumpyPreproc(fn=exponential_moving_demean, init_block_size=sfreq*10, factor_new=1/(sfreq*5)),
]
# Preprocess the data
preprocess(train_whole_set, preprocessors)
# Next, extract the 4-second trials from the dataset.
# Create windows using braindecode function for this. It needs parameters to define how
# trials should be used.
class_names = ['Right Hand', 'Rest'] # for later plotting
class_mapping = {'right_hand': 0, 'rest': 1}
windows_dataset = create_windows_from_events(
train_whole_set,
trial_start_offset_samples=0,
trial_stop_offset_samples=0,
preload=True,
mapping=class_mapping,
)
from torch.utils.data import Subset
n_split = int(np.round(0.75 * len(windows_dataset)))
valid_set = Subset(windows_dataset, range(n_split, len(windows_dataset)))
train_set = Subset(windows_dataset, range(0, n_split))
return train_set, valid_set
def test_predict_trials():
ds = MOABBDataset('BNCI2014001', subject_ids=1)
ds1 = ds.split([0])['0']
# determine original trial size
windows_ds1 = create_windows_from_events(
ds1,
)
trial_size = windows_ds1[0][0].shape[1]
# create two windows per trial, where windows maximally overlap
window_size_samples = trial_size - 1
window_stride_samples = 5
windows_ds1 = create_windows_from_events(
ds1,
window_size_samples=window_size_samples,
window_stride_samples=window_stride_samples,
drop_last_window=False,
)
in_chans = windows_ds1[0][0].shape[0]
n_classes = len(windows_ds1.get_metadata()['target'].unique())
model = ShallowFBCSPNet(
in_chans=in_chans,
n_classes=n_classes,
)
to_dense_prediction_model(model)
output_shape = get_output_shape(model, in_chans, window_size_samples)
# the number of samples required to get 1 output
receptive_field_size = window_size_samples - output_shape[-1] + 1
preds, targets = predict_trials(model, windows_ds1)
# some model, cropped data
assert preds.shape[-1] + receptive_field_size - 1 == trial_size
assert preds.shape[1] == n_classes
assert preds.shape[0] == targets.shape[0]
metadata = windows_ds1.get_metadata()
expected_targets = metadata[metadata['i_window_in_trial'] == 0][
'target'].values
np.testing.assert_array_equal(expected_targets, targets)
# some model, trialwise data
windows_ds2 = create_windows_from_events(ds1)
with pytest.warns(UserWarning, match='This function was designed to predict'
' trials from cropped datasets.'):
predict_trials(model, windows_ds2)
# cropped EEGClassifier, cropped data
clf = EEGClassifier(
model,
criterion=torch.nn.NLLLoss,
optimizer=optim.AdamW,
train_split=None,
optimizer__lr=0.0625 * 0.01,
optimizer__weight_decay=0,
batch_size=64,
)
clf.initialize()
clf.predict_trials(windows_ds1, return_targets=True)
# cropped EEGClassifier, trialwise data
with pytest.warns(UserWarning, match="This method was designed to predict "
"trials in cropped mode. Calling it "
"when cropped is False will give the "
"same result as '.predict'."):
clf.predict_trials(windows_ds2)
