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 _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
'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(
train_filename,
load_sensor_names=sensor_names).load() # all channels
cnt = cnt.drop_channels(sensor_names_robot_aux)
elif RobotEEG:
cnt = BBCIDataset(
train_filename,
load_sensor_names=sensor_names).load() # all channels
cnt = cnt.drop_channels(sensor_names_aux)
elif RobotEEGAux:
cnt = BBCIDataset(
train_filename,
load_sensor_names=sensor_names).load() # all channels
elif onlyAux:
cnt = BBCIDataset(
train_filename,
load_sensor_names=sensor_names_aux).load() # aux channels
elif RobotAux:
cnt = BBCIDataset(train_filename,