def load_file(file_name, standardize, demean):
bbci_set = BBCIDataset(file_name,
load_sensor_names=get_EEG_sensors_sorted())
log.info("Loading...")
cnt = bbci_set.load()
log.info("Set cz to zero and remove high absolute value trials")
marker_def = dict([(str(i_class), [i_class]) for i_class in xrange(1,5)])
clean_result = MaxAbsCleaner(threshold=800,
marker_def=marker_def,
segment_ival=[0,4000]).clean(cnt)
cnt = restrict_cnt(cnt, marker_def.values(), clean_result.clean_trials,
clean_result.rejected_chan_names, copy_data=False)
cnt = set_channel_to_zero(cnt, 'Cz')
log.info("Resampling...")
cnt = resample_cnt(cnt, newfs=250.0)
log.info("Car filtering...")
cnt = common_average_reference_cnt(cnt)
if standardize:
log.info("Standardizing...")
cnt = exponential_standardize_cnt(cnt)
if demean:
log.info("Demeaning...")
cnt = exponential_demean_cnt(cnt)
return cnt
def load_file(file_name, standardize, demean):
bbci_set = BBCIDataset(file_name,
load_sensor_names=get_EEG_sensors_sorted())
log.info("Loading...")
cnt = bbci_set.load()
log.info("Set cz to zero and remove high absolute value trials")
marker_def = dict([(str(i_class), [i_class]) for i_class in xrange(1, 5)])
clean_result = MaxAbsCleaner(threshold=800,
marker_def=marker_def,
segment_ival=[0, 4000]).clean(cnt)
cnt = restrict_cnt(cnt,
marker_def.values(),
clean_result.clean_trials,
clean_result.rejected_chan_names,
copy_data=False)
cnt = set_channel_to_zero(cnt, 'Cz')
log.info("Resampling...")
cnt = resample_cnt(cnt, newfs=250.0)
log.info("Car filtering...")
cnt = common_average_reference_cnt(cnt)
if standardize:
log.info("Standardizing...")
cnt = exponential_standardize_cnt(cnt)
if demean:
log.info("Demeaning...")
cnt = exponential_demean_cnt(cnt)
return cnt
def preprocess_test_set(self):
if self.sensor_names is not None:
self.sensor_names = sort_topologically(self.sensor_names)
self.test_cnt = select_channels(self.test_cnt, self.sensor_names)
if self.set_cz_to_zero is True:
self.test_cnt = set_channel_to_zero(self.test_cnt, 'Cz')
if self.resample_fs is not None:
self.test_cnt = resample_cnt(self.test_cnt, newfs=self.resample_fs)
if self.common_average_reference is True:
self.test_cnt = common_average_reference_cnt(self.test_cnt)
if self.standardize_cnt is True:
self.test_cnt = exponential_standardize_cnt(self.test_cnt)
def preprocess_test_set(self):
if self.sensor_names is not None:
self.sensor_names = sort_topologically(self.sensor_names)
self.test_cnt = select_channels(self.test_cnt, self.sensor_names)
if self.set_cz_to_zero is True:
self.test_cnt = set_channel_to_zero(self.test_cnt, 'Cz')
if self.resample_fs is not None:
self.test_cnt = resample_cnt(self.test_cnt, newfs=self.resample_fs)
if self.common_average_reference is True:
self.test_cnt = common_average_reference_cnt(self.test_cnt)
if self.standardize_cnt is True:
self.test_cnt = exponential_standardize_cnt(self.test_cnt)
def preprocess_set(self):
# only remove rejected channels now so that clean function can
# be called multiple times without changing cleaning results
self.cnt = select_channels(self.cnt, self.rejected_chan_names,
invert=True)
if self.sensor_names is not None:
# Note this does not respect order of sensor names,
# it selects chans form given sensor names
# but keeps original order
self.cnt = select_channels(self.cnt, self.sensor_names)
if self.set_cz_to_zero is True:
self.cnt = set_channel_to_zero(self.cnt, 'Cz')
if self.resample_fs is not None:
self.cnt = resample_cnt(self.cnt, newfs=self.resample_fs)
if self.common_average_reference is True:
self.cnt = common_average_reference_cnt(self.cnt)
if self.standardize_cnt is True:
self.cnt = exponential_standardize_cnt(self.cnt)
def preprocess_set(self):
# only remove rejected channels now so that clean function can
# be called multiple times without changing cleaning results
self.cnt = select_channels(self.cnt,
self.rejected_chan_names,
invert=True)
if self.sensor_names is not None:
# Note this does not respect order of sensor names,
# it selects chans form given sensor names
# but keeps original order
self.cnt = select_channels(self.cnt, self.sensor_names)
if self.set_cz_to_zero is True:
self.cnt = set_channel_to_zero(self.cnt, 'Cz')
if self.resample_fs is not None:
self.cnt = resample_cnt(self.cnt, newfs=self.resample_fs)
if self.common_average_reference is True:
self.cnt = common_average_reference_cnt(self.cnt)
if self.standardize_cnt is True:
self.cnt = exponential_standardize_cnt(self.cnt)
