from pyriemann.stats import PermutationTestTwoWay, PermutationTest
from pyriemann.estimation import Covariances
###############################################################################
## Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -2., 6.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [
read_raw_edf(f, preload=True, verbose=False)
for f in eegbci.load_data(subject, runs)
]
raw = concatenate_raws(raw_files)
# strip channel names
raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
from mne.event import find_events
from pyriemann.stats import PermutationTest
from pyriemann.estimation import Covariances
###############################################################################
## Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 3.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [read_raw_edf(f, preload=True,verbose=False) for f in eegbci.load_data(subject, runs) ]
raw = concatenate_raws(raw_files)
# strip channel names
raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
# Apply band-pass filter
raw.filter(7., 35., method='iir')
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
from mne.channels import read_layout
print(__doc__)
# #############################################################################
# # Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -1., 4.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_fnames = eegbci.load_data(subject, runs)
raw_files = [read_raw_edf(f, preload=True) for f in raw_fnames]
raw = concatenate_raws(raw_files)
# strip channel names
raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
# Apply band-pass filter
raw.filter(7., 30., method='iir')
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
from mne.event import find_events
from pyriemann.stats import PermutationTest
from pyriemann.estimation import CospCovariances
###############################################################################
# Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 3.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [read_raw_edf(f, preload=True, verbose=False)
for f in eegbci.load_data(subject, runs)]
raw = concatenate_raws(raw_files)
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=None, preload=True, add_eeg_ref=False, verbose=False)
labels = epochs.events[:, -1] - 2
# get epochs
epochs_data = epochs.get_data()
sns.lineplot(data=dfd, x="wlen", y="dist", hue="estimator", ax=ax)
ax.set_title("Distance to groundtruth covariance matrix")
ax.set_xlabel("Number of time samples")
ax.set_ylabel(r"$\delta(\Sigma, \hat{\Sigma})$")
plt.tight_layout()
###############################################################################
# Choice of estimator for motor imagery data
# -------------------------------------------
# Loading data from PhysioNet MI dataset, for subject 1.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [
read_raw_edf(f, preload=True, stim_channel="auto")
for f in eegbci.load_data(subject, runs)
]
raw = concatenate_raws(raw_files)
picks = pick_types(raw.info, eeg=True, exclude="bads")
# subsample elecs
picks = picks[::2]
# Apply band-pass filter
raw.filter(7.0, 35.0, method="iir", picks=picks, skip_by_annotation="edge")
events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3))
event_ids = dict(hands=2, feet=3)
###############################################################################
# Influence of shrinkage to estimate covariance
# -----------------------------------------------
from sklearn.cross_validation import cross_val_score, KFold
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
###############################################################################
# Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 2.
event_id = dict(hands=2, feet=3)
subject = 7
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [
read_raw_edf(f, preload=True) for f in eegbci.load_data(subject, runs)
]
raw = concatenate_raws(raw_files)
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
# subsample elecs
picks = picks[::2]
# Apply band-pass filter
raw.filter(7., 35., method='iir', picks=picks)
def importbdf(bdfname, nchans=34, refchans=['EXG1', 'EXG2'],
hptsname=None, mask=255, extrachans=[],
exclude=None, verbose=None):
"""Wrapper around mne-python to import BDF files
Parameters
----------
bdfname - Name of the biosemi .bdf filename with full path
nchans - Number of EEG channels (including references)
(Optional) By default, 34 (32 + 2 references)
refchans - list of strings with reference channel names
(Optional) By default ['EXG1','EXG2'].
Use None for average reference.
hptsname - Name of the electrode position file in .hpts format with path
(Optional) By default a 32 channel Biosemi layout is used. If
the nchans is >= 64 and < 96, a 64 channel Biosemi layout is
used. If nchans >= 96, a 96 channel biosemi layout is used.
Formats other than .hpts will also likely work, but behavior
may vary.
mask - Integer mask to use for trigger channel (Default is 255).
extrachans - Additional channels other than EEG and EXG that may be in the
bdf file. These will be marked as MISC in mne-python.
Specify as list of names.
excllude - List of channel names to exclude from importing
verbose - bool, str, int, or None (Optional)
The verbosity of messages to print. If a str, it can be either DEBUG,
INFO, WARNING, ERROR, or CRITICAL.
Returns
-------
raw - MNE raw data object of rereferences and preloaded data
eves - Event list (3 column array as required by mne.Epochs)
Requires
--------
mne-python module > release 0.7
"""
# Default HPTS file
if(hptsname is None):
anlffr_root = os.path.dirname(sys.modules['anlffr'].__file__)
if nchans >= 64 and nchans < 96:
logger.info('Number of channels is greater than 64.'
' Hence loading a 64 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi64'
montage = read_montage(kind=hptsname, path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans >= 96:
logger.info('Number of channels is greater than 96.'
' Hence loading a 96 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi96'
montage = read_montage(kind=hptsname, path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans == 2:
logger.info('Number of channels is 2.'
'Guessing ABR montage.')
montage = None
misc = []
else:
logger.info('Loading a default 32 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi32'
montage = read_montage(kind=hptsname, path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
misc += extrachans
raw = edf.read_raw_edf(bdfname, montage=montage, preload=True,
misc=misc, exclude=exclude, stim_channel='Status')
# Rereference
if refchans is not None:
sys.stdout.write('Re-referencing data to: ' + ' '.join(refchans))
(raw, ref_data) = set_eeg_reference(raw, refchans, copy=False)
raw.info['bads'] += refchans
else:
# Add average reference operator for possible use later
ave_ref_operator = make_eeg_average_ref_proj(raw.info, activate=False)
raw = raw.add_proj(ave_ref_operator)
eves = find_events(raw, shortest_event=1, mask=mask)
return (raw, eves)
def get_score(subject=7):
###############################################################################
# Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 2.
event_id = dict(hands=2, feet=3)
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [
read_raw_edf(f, preload=True) for f in eegbci.load_data(subject, runs)
]
raw = concatenate_raws(raw_files)
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
# subsample elecs
picks = picks[::2]
# Apply band-pass filter
raw.filter(7., 35., method='iir', picks=picks)
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=None,
preload=True,
verbose=False)
labels = epochs.events[:, -1] - 2
# cross validation
cv = KFold(len(labels), 10, shuffle=True, random_state=42)
# get epochs
epochs_data_train = 1e6 * epochs.get_data()
# compute covariance matrices
cov_data_train = Covariances().transform(epochs_data_train)
###############################################################################
# Classification with Minimum distance to mean
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
# Use scikit-learn Pipeline with cross_val_score function
mdm.fit(cov_data_train, labels)
print(123)
###############################################################################
# Classification with Tangent Space Logistic Regression
clf = TSclassifier()
# Use scikit-learn Pipeline with cross_val_score function
scores = cross_val_score(clf, cov_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
ts_score = np.mean(scores)
print("Tangent space Classification accuracy: %f / Chance level: %f" %
(ts_score, class_balance))
###############################################################################
return [subject, mdm_score, ts_score]
def edf_loader(self, single_file):
raw = read_raw_edf(single_file, preload=True)
# print "raw: "
# print raw
# print "number of channels: %d, type: %s, raw.ch_names: " % (len(raw.ch_names), type(raw.ch_names))
# print raw.ch_names
start, stop = raw.time_as_index([0, 150])
ch_start = 2
ch_end = 16
data, times = raw[ch_start:ch_end, start:stop]
# print data.shape
# print type(data)
# print raw.ch_names[2:16]
# print "TTTTTTTTTTTTTTTTTTTTTTT"
# print type(times)
# print np.shape(times)
# print times
# print "TTTTTTTTTTTTTTTTTTTTTTT"
cha_names = []
for n in raw.ch_names:
str_n = str(n)
# str_n = str(unicodedata.normalize('NFKD', n).encode('ascii', 'ignore'))
cha_names.append(str_n)
# print type(raw.ch_names[0])
# print "cha_names: "
# print cha_names
data_dict = {i[0]: i[1:] for i in zip(cha_names[2:16], data)}
# print "data: "
# print data
# print "data_dict['AF3']: "
# print len(data_dict['AF3'][0])
d_dict = {}
for k, v in data_dict.iteritems():
d_dict[k] = v[0]
# print "d_dict['AF3']:"
# print len(d_dict['AF3'])
raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
raw.filter(2., 30., method='iir')
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
print "events: "
print events
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
exclude='bads')
"""
epochs = Epochs(raw, events, self.event_id, self.tmin, self.tmax, proj=True, picks=picks, baseline=None, preload=True, add_eeg_ref=False)
epoch_train = epochs.crop(tmin=1., tmax=2., copy=True)
labels = epochs.event[:, -1] - 2
"""
# raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
# epochs = Epochs()
# print "picks: "
# print picks
# print raw[picks]
# print type(raw)
# print raw.info
# print d_dict
# print d_dict.keys()
return d_dict
from mne.event import find_events
from mne.decoding import CSP
from mne.layouts import read_layout
###############################################################################
## Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -1.0, 4.0
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_fnames = eegbci.load_data(subject, runs)
raw_files = [read_raw_edf(f, tal_channel=-1, preload=True) for f in raw_fnames]
raw = concatenate_raws(raw_files)
# strip channel names
raw.info["ch_names"] = [chn.strip(".") for chn in raw.info["ch_names"]]
# Apply band-pass filter
raw.filter(7.0, 30.0, method="iir")
events = find_events(raw, shortest_event=0, stim_channel="STI 014")
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False, exclude="bads")
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(
def importbdf(bdfname,
nchans=34,
refchans=['EXG1', 'EXG2'],
hptsname=None,
mask=255,
extrachans=[],
exclude=None,
verbose=None):
"""Wrapper around mne-python to import BDF files
Parameters
----------
bdfname - Name of the biosemi .bdf filename with full path
nchans - Number of EEG channels (including references)
(Optional) By default, 34 (32 + 2 references)
refchans - list of strings with reference channel names
(Optional) By default ['EXG1','EXG2'].
Use None for average reference.
hptsname - Name of the electrode position file in .hpts format with path
(Optional) By default a 32 channel Biosemi layout is used. If
the nchans is >= 64 and < 96, a 64 channel Biosemi layout is
used. If nchans >= 96, a 96 channel biosemi layout is used.
Formats other than .hpts will also likely work, but behavior
may vary.
mask - Integer mask to use for trigger channel (Default is 255).
extrachans - Additional channels other than EEG and EXG that may be in the
bdf file. These will be marked as MISC in mne-python.
Specify as list of names.
excllude - List of channel names to exclude from importing
verbose - bool, str, int, or None (Optional)
The verbosity of messages to print. If a str, it can be either DEBUG,
INFO, WARNING, ERROR, or CRITICAL.
Returns
-------
raw - MNE raw data object of rereferences and preloaded data
eves - Event list (3 column array as required by mne.Epochs)
Requires
--------
mne-python module > release 0.7
"""
# Default HPTS file
if (hptsname is None):
anlffr_root = os.path.dirname(sys.modules['anlffr'].__file__)
if nchans >= 64 and nchans < 96:
logger.info('Number of channels is greater than 64.'
' Hence loading a 64 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi64'
montage = read_montage(kind=hptsname,
path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans >= 96:
logger.info('Number of channels is greater than 96.'
' Hence loading a 96 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi96'
montage = read_montage(kind=hptsname,
path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans == 2:
logger.info('Number of channels is 2.'
'Guessing ABR montage.')
montage = None
misc = []
else:
logger.info('Loading a default 32 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi32'
montage = read_montage(kind=hptsname,
path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
misc += extrachans
raw = edf.read_raw_edf(bdfname,
montage=montage,
preload=True,
misc=misc,
exclude=exclude,
stim_channel='Status')
# Rereference
if refchans is not None:
sys.stdout.write('Re-referencing data to: ' + ' '.join(refchans))
(raw, ref_data) = set_eeg_reference(raw, refchans, copy=False)
raw.info['bads'] += refchans
else:
# Add average reference operator for possible use later
ave_ref_operator = make_eeg_average_ref_proj(raw.info, activate=False)
raw = raw.add_proj(ave_ref_operator)
eves = find_events(raw, shortest_event=1, mask=mask)
return (raw, eves)
from sklearn.cross_validation import cross_val_score, KFold
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.lda import LDA
###############################################################################
# Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 2.
event_id = dict(hands=2, feet=3)
subject = 7
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [read_raw_edf(f, preload=True) for f in eegbci.load_data(subject,
runs)]
raw = concatenate_raws(raw_files)
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# subsample elecs
picks = picks[::2]
# Apply band-pass filter
raw.filter(7., 35., method='iir', picks=picks)
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
from mne.event import find_events
from mne.decoding import CSP
from mne.layouts import read_layout
###############################################################################
## Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -1., 4.
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_fnames = eegbci.load_data(subject, runs)
raw_files = [read_raw_edf(f, tal_channel=-1, preload=True) for f in raw_fnames]
raw = concatenate_raws(raw_files)
# strip channel names
raw.info['ch_names'] = [chn.strip('.') for chn in raw.info['ch_names']]
# Apply band-pass filter
raw.filter(7., 30., method='iir')
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
# sklearn imports
from sklearn.cross_validation import cross_val_score, KFold
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
###############################################################################
# Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 2.
event_id = dict(hands=2, feet=3)
subject = 7
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [read_raw_edf(f, preload=True) for f in eegbci.load_data(subject,
runs)]
raw = concatenate_raws(raw_files)
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
exclude='bads')
# subsample elecs
picks = picks[::2]
# Apply band-pass filter
raw.filter(7., 35., method='iir', picks=picks)
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
from mne.channels import read_layout
print(__doc__)
# #############################################################################
# # Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = -1.0, 4.0
event_id = dict(hands=2, feet=3)
subject = 1
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_fnames = eegbci.load_data(subject, runs)
raw_files = [read_raw_edf(f, preload=True) for f in raw_fnames]
raw = concatenate_raws(raw_files)
# strip channel names
raw.info["ch_names"] = [chn.strip(".") for chn in raw.info["ch_names"]]
# Apply band-pass filter
raw.filter(7.0, 30.0, method="iir")
events = find_events(raw, shortest_event=0, stim_channel="STI 014")
picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False, exclude="bads")
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(
