def get_data():
import os
os.sys.path.append('/home/schirrmr/braindecode/code/braindecode/')
from braindecode.datautil.trial_segment import create_signal_target_from_raw_mne
from braindecode.datasets.bbci import BBCIDataset
from braindecode.mne_ext.signalproc import mne_apply, resample_cnt
from braindecode.datautil.signalproc import exponential_running_standardize
subject_id = 4 # 1-14
loader = BBCIDataset(
'/data/schirrmr/schirrmr/HGD-public/reduced/train/{:d}.mat'.format(
subject_id),
load_sensor_names=['C3'])
cnt = loader.load()
cnt = cnt.drop_channels(['STI 014'])
from collections import OrderedDict
marker_def = OrderedDict([('Right Hand', [1]), (
'Left Hand',
[2],
), ('Rest', [3]), ('Feet', [4])])
# Here you can choose a larger sampling rate later
# Right now chosen very small to allow fast initial experiments
cnt = resample_cnt(cnt, new_fs=500)
cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, cnt)
ival = [0, 2000] # ms to cut trial
dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival)
return dataset.X, dataset.y
def load_data(filenames, sensor_names, name_to_start_codes,
name_to_stop_codes, trial_ival, break_ival,
min_break_length_ms, max_break_length_ms, input_time_length,
filename_to_extra_args):
all_sets = []
original_args = locals()
for filename in filenames:
kwargs = deepcopy(original_args)
if filename in filename_to_extra_args:
kwargs.update(filename_to_extra_args[filename])
log.info("Loading {:s}...".format(filename))
cnt = BBCIDataset(filename, load_sensor_names=sensor_names).load()
cnt = cnt.drop_channels(['STI 014'])
log.info("Resampling...")
cnt = resample_cnt(cnt, 100)
log.info("Standardizing...")
cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, init_block_size=50).T, cnt)
log.info("Transform to set...")
full_set = (create_signal_target_with_breaks_from_mne(
cnt,
kwargs['name_to_start_codes'],
kwargs['trial_ival'],
kwargs['name_to_stop_codes'],
kwargs['min_break_length_ms'],
kwargs['max_break_length_ms'],
kwargs['break_ival'],
prepad_trials_to_n_samples=kwargs['input_time_length'],
))
all_sets.append(full_set)
return all_sets
def load_bbci_data(filename, low_cut_hz):
load_sensor_names = None
loader = BBCIDataset(filename, load_sensor_names=load_sensor_names)
log.info("Loading data...")
cnt = loader.load()
# Cleaning: First find all trials that have absolute microvolt values
# larger than +- 800 inside them and remember them for removal later
log.info("Cutting trials...")
marker_def = OrderedDict([('Right Hand', [1]), ('Left Hand', [2],),
('Rest', [3]), ('Feet', [4])])
clean_ival = [0, 4000]
set_for_cleaning = create_signal_target_from_raw_mne(cnt, marker_def,
clean_ival)
clean_trial_mask = np.max(np.abs(set_for_cleaning.X), axis=(1, 2)) < 800
log.info("Clean trials: {:3d} of {:3d} ({:5.1f}%)".format(
np.sum(clean_trial_mask),
len(set_for_cleaning.X),
np.mean(clean_trial_mask) * 100))
# now pick only sensors with C in their name
# as they cover motor cortex
C_sensors = ['FC5', 'FC1', 'FC2', 'FC6', 'C3', 'C4', 'CP5',
'CP1', 'CP2', 'CP6', 'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2',
'C6',
'CP3', 'CPz', 'CP4', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h',
'FCC5h',
'FCC3h', 'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h',
'CPP5h',
'CPP3h', 'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h',
'CCP1h',
'CCP2h', 'CPP1h', 'CPP2h']
cnt = cnt.pick_channels(C_sensors)
# Further preprocessings
log.info("Resampling...")
cnt = resample_cnt(cnt, 250.0)
print("REREFERENCING")
log.info("Highpassing...")
cnt = mne_apply(lambda a: highpass_cnt(a, low_cut_hz, cnt.info['sfreq'], filt_order=3, axis=1),cnt)
log.info("Standardizing...")
cnt = mne_apply(lambda a: exponential_running_standardize(a.T, factor_new=1e-3,init_block_size=1000,eps=1e-4).T,cnt)
# Trial interval, start at -500 already, since improved decoding for networks
ival = [-500, 4000]
dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival)
dataset.X = dataset.X[clean_trial_mask]
dataset.y = dataset.y[clean_trial_mask]
return dataset.X, dataset.y
def _create_examples(self):
name_to_code = OrderedDict([('Right', 1), ('Left', 2), ('Rest', 3),
('Feet', 4)])
data_list_list = []
for file_name in self.file_names:
cnt = BBCIDataset(file_name,
load_sensor_names=self.load_sensor_names).load()
cnt = cnt.drop_channels(['STI 014'])
cnt = resample_cnt(cnt, self.sampling_freq)
if self.normalization_type == 'exponential':
cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, init_block_size=1000, factor_new=0.001, eps=1e-4).
T, cnt)
data = create_signal_target_from_raw_mne(cnt, name_to_code,
self.segment_ival_ms)
data_list = [(d, l) for d, l in zip(data.X, data.y)]
data_list = self.cv_split(data_list)
# Normalize the data
if self.normalization_type == 'standard':
running_statistics = RunningStatistics(
dim=data_list[0][0].shape[0], time_dimension_first=False)
for data, label in data_list:
running_statistics.append(data)
mean = running_statistics.mean_vector()
sdev = np.clip(np.sqrt(running_statistics.var_vector()), 1e-5,
None)
logger.info('Normalize with \n mean: %s, \n sdev: %s' %
(mean, sdev))
for i in range(len(data_list)):
data_list[i] = ((data_list[i][0] - mean) / sdev,
data_list[i][1])
data_list_list.append(data_list)
# Create examples for 4 classes
for i, data_list in enumerate(data_list_list):
for label in range(4):
class_data_list = [
data for data in data_list if data[1] == label
]
self.examples.append([
BBCIDataReaderMulti.ExampleInfo(
example_id=str((i, label, j)),
random_mode=self.random_mode,
offset_size=self.offset_size,
label=label,
data=data,
context=i)
for (j, (data, label)) in enumerate(class_data_list)
])
def run_exp(filename, min_freq, max_freq, low_width, high_width, high_overlap,
last_low_freq, low_overlap, n_top_bottom_csp_filters,
n_selected_features, sensors):
if sensors == 'all':
cnt = BBCIDataset(filename).load()
else:
assert sensors == 'C_sensors'
sensor_names = [
'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'Cz', 'C4', 'CP5', 'CP1', 'CP2',
'CP6', 'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6', 'CP3', 'CPz',
'CP4', 'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h', 'FCC3h',
'FCC4h', 'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h',
'CPP3h', 'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h',
'CCP1h', 'CCP2h', 'CPP1h', 'CPP2h'
]
cnt = BBCIDataset(filename, load_sensor_names=sensor_names).load()
cnt = cnt.drop_channels(['STI 014'])
name_to_start_codes = OrderedDict([('Left Hand', [1]), (
'Foot',
[2],
), ('Right Hand', [3]), ('Word', [4]), ('Mental Rotation', 5),
('Rest', 6)])
name_to_stop_codes = OrderedDict([('Left Hand', [10]), (
'Foot',
[20],
), ('Right Hand', [30]), ('Word', [40]), ('Mental Rotation', 50),
('Rest', 60)])
csp_experiment = CSPExperiment(
cnt,
name_to_start_codes,
epoch_ival_ms=[500, 0],
name_to_stop_codes=name_to_stop_codes,
min_freq=min_freq,
max_freq=max_freq,
last_low_freq=last_low_freq,
low_width=low_width,
high_width=high_width,
low_overlap=low_overlap,
high_overlap=high_overlap,
filt_order=4,
n_folds=5,
n_top_bottom_csp_filters=n_top_bottom_csp_filters,
# this number times two will be number of csp filters per filterband before feature selection
n_selected_filterbands=None, # how many filterbands to select?
n_selected_features=n_selected_features,
# how many Features to select with the feature selection?
forward_steps=2, # feature selection param
backward_steps=1, # feature selection param
stop_when_no_improvement=False, # feature selection param
only_last_fold=True,
# Split into number of folds, but only run the last fold (i.e. last fold as test fold)?
restricted_n_trials=None,
# restrict to certain number of _clean_ trials?
shuffle=False, # shuffle or do blockwise folds?
low_bound=0.)
csp_experiment.run()
return csp_experiment
def load_file(filename, car=True, load_sensor_names=None):
cnt = BBCIDataset(filename, load_sensor_names=load_sensor_names).load()
cnt = cnt.drop_channels(["STI 014"])
if car:
def car(a):
return a - np.mean(a, keepdims=True, axis=0)
cnt = mne_apply(car, cnt)
return cnt
def load_cnt(file_path, channel_names, clean_on_all_channels=True):
# if we have to run the cleaning procedure on all channels, putting
# load_sensor_names to None will assure us the BBCIDataset class will
# load all possible sensors
if clean_on_all_channels is True:
channel_names = None
# create the loader object for BBCI standard
loader = BBCIDataset(file_path, load_sensor_names=channel_names)
# load data
return loader.load()
def load_bbci_data(filename, low_cut_hz, debug=False):
load_sensor_names = None
if debug:
load_sensor_names = ['C3', 'C4', 'C2']
loader = BBCIDataset(filename, load_sensor_names=load_sensor_names)
log.info("Loading data...")
cnt = loader.load()
log.info("Cutting trials...")
marker_def = OrderedDict([('Right Hand', [1]), (
'Left Hand',
[2],
), ('Rest', [3]), ('Feet', [4])])
clean_ival = [0, 4000]
set_for_cleaning = create_signal_target_from_raw_mne(
cnt, marker_def, clean_ival)
clean_trial_mask = np.max(np.abs(set_for_cleaning.X), axis=(1, 2)) < 800
log.info("Clean trials: {:3d} of {:3d} ({:5.1f}%)".format(
np.sum(clean_trial_mask), len(set_for_cleaning.X),
np.mean(clean_trial_mask) * 100))
# lets convert to millivolt for numerical stability of next operations
C_sensors = [
'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'C4', 'CP5', 'CP1', 'CP2', 'CP6',
'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6', 'CP3', 'CPz', 'CP4',
'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h', 'FCC3h', 'FCC4h', 'FCC6h',
'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h', 'CPP3h', 'CPP4h', 'CPP6h',
'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h', 'CCP1h', 'CCP2h', 'CPP1h', 'CPP2h'
]
if debug:
C_sensors = load_sensor_names
cnt = cnt.pick_channels(C_sensors)
cnt = mne_apply(lambda a: a * 1e6, cnt)
log.info("Resampling...")
cnt = resample_cnt(cnt, 250.0)
log.info("Highpassing...")
cnt = mne_apply(
lambda a: highpass_cnt(
a, low_cut_hz, cnt.info['sfreq'], filt_order=3, axis=1), cnt)
log.info("Standardizing...")
cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, cnt)
ival = [-500, 4000]
dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival)
return dataset
def import_EEGData_test(start=0, end=9, dir='../data_HGD/test/'):
X, y = [], []
for i in range(start, end):
dataFile = str(dir + str(i + 1) + '.mat')
print("File:", dataFile, " loading...")
cnt = BBCIDataset(filename=dataFile, load_sensor_names=None).load()
marker_def = OrderedDict([('Right Hand', [1]), (
'Left Hand',
[2],
), ('Rest', [3]), ('Feet', [4])])
clean_ival = [0, 4000]
set_for_cleaning = create_signal_target_from_raw_mne(
cnt, marker_def, clean_ival)
clean_trial_mask = np.max(np.abs(set_for_cleaning.X),
axis=(1, 2)) < 800
C_sensors = [
'FC5', 'FC1', 'FC2', 'FC6', 'C3', 'C4', 'CP5', 'CP1', 'CP2', 'CP6',
'FC3', 'FCz', 'FC4', 'C5', 'C1', 'C2', 'C6', 'CP3', 'CPz', 'CP4',
'FFC5h', 'FFC3h', 'FFC4h', 'FFC6h', 'FCC5h', 'FCC3h', 'FCC4h',
'FCC6h', 'CCP5h', 'CCP3h', 'CCP4h', 'CCP6h', 'CPP5h', 'CPP3h',
'CPP4h', 'CPP6h', 'FFC1h', 'FFC2h', 'FCC1h', 'FCC2h', 'CCP1h',
'CCP2h', 'CPP1h', 'CPP2h'
]
cnt = cnt.pick_channels(C_sensors)
cnt = resample_cnt(cnt, 250.0)
cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, cnt)
ival = [-500, 4000]
dataset = create_signal_target_from_raw_mne(cnt, marker_def, ival)
dataset.X = dataset.X[clean_trial_mask]
dataset.X = dataset.X[:, :, np.newaxis, :]
dataset.y = dataset.y[clean_trial_mask]
dataset.y = dataset.y[:, np.newaxis]
X.extend(dataset.X)
y.extend(dataset.y)
X = data_in_one(np.array(X))
y = np.array(y)
print("X:", X.shape)
print("y:", y.shape)
dataset = EEGDataset(X, y)
return dataset
timeWindowDuration = 3661 #ms
# deep4 stride 2: 1825
# deep4 stride 3: 3661
elif ResNet:
timeWindowDuration = 1005 #ms
saveAddonText = saveAddonText + '_ResNet'
elif EEGNet_v4:
timeWindowDuration = 1335
saveAddonText = saveAddonText + '_EEGNetv4'
for subjName in Subjects:
train_filename = './data/BBCIformat/' + subjName + '_' + str(
samplingRate) + 'Hz_CAR.BBCI.mat'
sensor_names = BBCIDataset.get_all_sensors(train_filename,
pattern=None)
sensor_names_aux = ['ECG', 'Respiration']
sensor_names_robot = [
'robotHandPos_x', 'robotHandPos_y', 'robotHandPos_z'
]
sensor_names_robot_aux = [
'robotHandPos_x', 'robotHandPos_y', 'robotHandPos_z', 'ECG',
'Respiration'
]
if onlyRobotData:
cnt = BBCIDataset(train_filename,
load_sensor_names=sensor_names_robot).load(
) # robot pos channels
elif onlyEEGData:
cnt = BBCIDataset(
timeWindowDuration = 1335
saveAddonText = saveAddonText + '_EEGNetv4'
saveAddonText_tmp = saveAddonText_orig + '_onlyRobotData'
cnt = []
train_sets = []
test_sets = []
# load and process data
for iSubject, subjName in enumerate(Subjects):
train_filename = './data/BBCIformat/' + subjName + '_' + str(
samplingRate) + 'Hz_CAR.BBCI.mat'
sensor_names = BBCIDataset.get_all_sensors(train_filename, pattern=None)
sensor_names_aux = ['ECG', 'Respiration']
sensor_names_robot = ['robotHandPos_x', 'robotHandPos_y', 'robotHandPos_z']
sensor_names_robot_aux = [
'robotHandPos_x', 'robotHandPos_y', 'robotHandPos_z', 'ECG',
'Respiration'
]
if onlyRobotData:
cnt.append(
BBCIDataset(train_filename, load_sensor_names=sensor_names_robot).
load()) # robot pos channels
elif onlyEEGData:
cnt.append(
BBCIDataset(train_filename,
load_sensor_names=sensor_names).load()) # all channels
