def determine_sensors(self):
#TODELAY: change to only taking filename? maybe more
# clarity where file is opened
all_sensor_names = self.get_all_sensors(self.filename, pattern=None)
if self.load_sensor_names is None:
# if no sensor names given, take all EEG-chans
EEG_sensor_names = all_sensor_names
EEG_sensor_names = filter(lambda s: not s.startswith('BIP'),
EEG_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('E'),
EEG_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('Microphone'),
EEG_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('Breath'),
EEG_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('GSR'),
EEG_sensor_names)
assert (len(EEG_sensor_names) == 128 or len(EEG_sensor_names) == 64
or len(EEG_sensor_names) == 32
or len(EEG_sensor_names) == 16), (
"Recheck this code if you have different sensors...")
# sort sensors topologically to allow networks to exploit topology
# this is kpe there to ensure reproducibility,
# rerunning of old results only
self.load_sensor_names = sort_topologically(EEG_sensor_names)
chan_inds = self.determine_chan_inds(all_sensor_names,
self.load_sensor_names)
return chan_inds, self.load_sensor_names
def __init__(self, signal_processor,
sensor_names='all',
axes=('b', 'c', 0, 1),
sort_topological=True,
end_marker_def=None,
marker_cutter=None):
# sort sensors topologically to allow networks to exploit topology
if (sensor_names is not None) and (sensor_names != 'all') and sort_topological:
sensor_names = sort_topologically(sensor_names)
self.__dict__.update(locals())
del self.self
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 __init__(
self,
signal_processor,
sensor_names="all",
axes=("b", "c", 0, 1),
sort_topological=True,
end_marker_def=None,
marker_cutter=None,
):
# sort sensors topologically to allow networks to exploit topology
if (sensor_names is not None) and (sensor_names != "all") and sort_topological:
sensor_names = sort_topologically(sensor_names)
self.__dict__.update(locals())
del self.self
def __init__(self,
signal_processor,
sensor_names='all',
limits=None,
start=None,
stop=None,
axes=('b', 'c', 0, 1),
unsupervised_preprocessor=None,
sort_topological=True):
# sort sensors topologically to allow networks to exploit topology
if (sensor_names is not None) and (sensor_names
is not 'all') and sort_topological:
sensor_names = sort_topologically(sensor_names)
self.__dict__.update(locals())
del self.self
def determine_sensors(self):
#TODELAY: change to only taking filename? maybe more
# clarity where file is opened
all_sensor_names = self.get_all_sensors(self.filename, pattern=None)
if self.load_sensor_names is None:
# if no sensor names given, take all EEG-chans
EEG_sensor_names = filter(lambda s: not s.startswith('E'), all_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('Microphone'), EEG_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('Breath'), EEG_sensor_names)
EEG_sensor_names = filter(lambda s: not s.startswith('GSR'), EEG_sensor_names)
assert (len(EEG_sensor_names) == 128 or
len(EEG_sensor_names) == 64 or
len(EEG_sensor_names) == 32 or
len(EEG_sensor_names) == 16), (
"Recheck this code if you have different sensors...")
# sort sensors topologically to allow networks to exploit topology
self.load_sensor_names = sort_topologically(EEG_sensor_names)
chan_inds = self.determine_chan_inds(all_sensor_names,
self.load_sensor_names)
return chan_inds, self.load_sensor_names
