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 load(self):
cnt = self.set_loaders[0].load()
for loader in self.set_loaders[1:]:
next_cnt = loader.load()
# always sample down to lowest common denominator
if next_cnt.fs > cnt.fs:
log.warn("Next set has larger sampling rate ({:d}) "
"than before ({:d}), resampling next set".format(
next_cnt.fs, cnt.fs))
next_cnt = resample_cnt(next_cnt, cnt.fs)
if next_cnt.fs < cnt.fs:
log.warn("Next set has smaller sampling rate ({:d}) "
"than before ({:d}), resampling set so far".format(
next_cnt.fs, cnt.fs))
cnt = resample_cnt(cnt, next_cnt.fs)
cnt = append_cnt(cnt, next_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)
def send_file_data():
print("Loading Experiment...")
# Use model to get cnt preprocessors
base_name = 'data/models/online/cnt/start-end-mrk/125'
exp = create_experiment(base_name + '.yaml')
print("Loading File...")
offline_execution_set = BBCIDataset(
'data/robot-hall/NiRiNBD15_cursor_250Hz.BBCI.mat',
load_sensor_names=get_nico_sensors())
cnt = offline_execution_set.load()
log.info("Preprocessing...")
cnt = resample_cnt(cnt, newfs=100)
cnt = lowpass_cnt(cnt, high_cut_off_hz=40, filt_order=10)
log.info("Done.")
cnt_data = cnt.data.astype(np.float32)
assert not np.any(np.isnan(cnt_data))
assert not np.any(np.isinf(cnt_data))
assert not np.any(np.isneginf(cnt_data))
print("max possible block", np.ceil(len(cnt_data) / 50.0))
segmenter = MarkerSegmenter(
segment_ival=(500, 0),
marker_def={
'Right Hand': [1],
'Feet': [2],
'Rotation': [3],
'Words': [4],
'Rest': [5]
},
trial_classes=['Right Hand', 'Feet', 'Rotation', 'Words', 'Rest'],
end_marker_def={
'Right Hand': [10],
'Feet': [20],
'Rotation': [30],
'Words': [40],
'Rest': [50],
},
)
cnt_y, class_names = segmenter.segment(cnt)
has_marker = np.sum(cnt_y, axis=1) > 0
new_y = np.zeros(cnt_y.shape[0], dtype=np.int32)
new_y[has_marker] = (np.argmax(cnt_y[has_marker], axis=1) + 1)
print("Done.")
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect(("127.0.0.1", 7987))
chan_names = [
'Fp1', 'Fpz', 'Fp2', 'AF7', 'AF3', 'AF4', 'AF8', 'F7', 'F5', 'F3',
'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'FT7', 'FC5', 'FC3', 'FC1', 'FCz',
'FC2', 'FC4', 'FC6', 'FT8', 'M1', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2',
'C4', 'C6', 'T8', 'M2', 'TP7', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2',
'CP4', 'CP6', 'TP8', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6',
'P8', 'PO7', 'PO5', 'PO3', 'POz', 'PO4', 'PO6', 'PO8', 'O1', 'Oz',
'O2', 'marker'
]
chan_line = " ".join(chan_names) + "\n"
s.send(chan_line)
n_chans = 65
n_samples = 50
s.send(np.array([n_chans], dtype=np.int32).tobytes())
s.send(np.array([n_samples], dtype=np.int32).tobytes())
print("Sending data...")
i_block = 0 # if setting i_block to sth higher, printed results will incorrect
max_stop_block = np.ceil(len(cnt_data) / float(n_samples))
stop_block = 800
assert stop_block < max_stop_block
while i_block < stop_block:
arr = cnt_data[i_block * n_samples:i_block * n_samples +
n_samples, :].T
this_y = new_y[i_block * n_samples:i_block * n_samples + n_samples]
# chan x time
arr = np.concatenate((arr, this_y[np.newaxis, :]),
axis=0).astype(np.float32)
s.send(arr.tobytes(order='F'))
assert arr.shape == (n_chans, n_samples)
i_block += 1
gevent.sleep(0.03)
print("Done.")
return cnt
