#
eegnb_data_path = os.path.join(os.path.expanduser('~/'), '.eegnb', 'data')
n170_data_path = os.path.join(eegnb_data_path, 'visual-N170', 'eegnb_examples')
# If dataset hasn't been downloaded yet, download it
if not os.path.isdir(n170_data_path):
fetch_dataset(data_dir=eegnb_data_path,
experiment='visual-N170',
site='eegnb_examples')
subject = 1
session = 1
raw = load_data(subject,
session,
experiment='visual-N170',
site='eegnb_examples',
device_name='muse2016',
data_dir=eegnb_data_path)
###################################################################################################
###################################################################################################
# Filteriing
# ----------------------------
raw.filter(1, 30, method='iir')
###################################################################################################
# Epoching
# ----------------------------
# the example dataset, if you do not already have it.
#
###################################################################################################
eegnb_data_path = os.path.join(os.path.expanduser('~/'),'.eegnb', 'data')
ssvep_data_path = os.path.join(eegnb_data_path, 'visual-SSVEP', 'eegnb_examples')
# If dataset hasn't been downloaded yet, download it
if not os.path.isdir(ssvep_data_path):
fetch_dataset(data_dir=eegnb_data_path, experiment='visual-SSVEP', site='eegnb_examples');
subject = 1
session = 1
raw = load_data(subject, session,
experiment='visual-SSVEP', site='eegnb_examples', device_name='muse2016',
data_dir = eegnb_data_path,
replace_ch_names={'Right AUX': 'POz'})
###################################################################################################
# Visualize the power spectrum
# ----------------------------
raw.plot_psd()
###################################################################################################
# Epoching
# ----------------------------
# Next, we will chunk (epoch) the data into segments representing the data 100ms before to 800ms after each stimulus.
# Note: we will not reject epochs here because the amplitude of the SSVEP at POz is so large it is difficult to separate from eye blinks
cueing_data_path = os.path.join(eegnb_data_path, 'visual-cueing',
'kylemathlab_dev')
# If dataset hasn't been downloaded yet, download it
if not os.path.isdir(cueing_data_path):
fetch_dataset(data_dir=eegnb_data_path,
experiment='visual-cueing',
site='kylemathlab_dev')
subject = 1
session = 1
sub = 302
raw = load_data(eegnb_data_path,
experiment='visual-cueing',
site='kylemathlab_dev',
sfreq=256.,
subject_nb=sub,
session_nb=1)
raw.append(
load_data(eegnb_data_path,
experiment='visual-cueing',
site='kylemathlab_dev',
sfreq=256.,
subject_nb=sub,
session_nb=2))
###################################################################################################
# Visualize the power spectrum
# ----------------------------
#
# ---------------------
#
# ( See the n170 `load_and_visualize` example for further description of this)
#
eegnb_data_path = os.path.join(os.path.expanduser('~/'),'.eegnb', 'data')
n170_data_path = os.path.join(eegnb_data_path, 'visual-N170', 'eegnb_examples')
# If dataset hasn't been downloaded yet, download it
if not os.path.isdir(n170_data_path):
fetch_dataset(data_dir=eegnb_data_path, experiment='visual-N170', site='eegnb_examples')
subject = 1
session = 1
raw = load_data(eegnb_data_path,
experiment='visual-N170', site='eegnb_examples', device='muse2016',
sfreq=256.,
subject_nb=subject, session_nb=session)
###################################################################################################
# Filteriing
# ----------------------------
raw.filter(1,30, method='iir')
###################################################################################################
# Epoching
# ----------------------------
# Create an array containing the timestamps and type of each stimulus (i.e. face or house)
events = find_events(raw)
event_id = {'House': 1, 'Face': 2}
sub_count += 1
if (sub_count in really_bad_subs):
rej_thresh_uV = 90
elif (sub_count in bad_subs):
rej_thresh_uV = 90
else:
rej_thresh_uV = 90
rej_thresh = rej_thresh_uV * 1e-6
# Load both sessions
raw = load_data(
sub,
1, # subject, session
experiment='visual-cueing',
site='kylemathlab_dev',
device_name='muse2016',
data_dir=eegnb_data_path)
raw.append(
load_data(
sub,
2, # subject, session
experiment='visual-cueing',
site='kylemathlab_dev',
device_name='muse2016',
data_dir=eegnb_data_path))
# Filter Raw Data
raw.filter(1, 30, method='iir')
#
eegnb_data_path = os.path.join(os.path.expanduser('~/'), '.eegnb', 'data')
p300_data_path = os.path.join(eegnb_data_path, 'visual-P300', 'eegnb_examples')
# If dataset hasn't been downloaded yet, download it
if not os.path.isdir(p300_data_path):
fetch_dataset(data_dir=eegnb_data_path,
experiment='visual-P300',
site='eegnb_examples')
subject = 1
session = 1
raw = load_data(eegnb_data_path,
experiment='visual-P300',
sfreq=256.,
subject_nb=subject,
session_nb=session)
###################################################################################################
# Filteriing
# ----------------------------
raw.filter(1, 30, method='iir')
###################################################################################################
# Epoching
# ----------------------------
# Create an array containing the timestamps and type of each stimulus (i.e. face or house)
events = find_events(raw)
ssvep_data_path = os.path.join(eegnb_data_path, 'visual-SSVEP',
'eegnb_examples')
# If dataset hasn't been downloaded yet, download it
if not os.path.isdir(ssvep_data_path):
fetch_dataset(data_dir=eegnb_data_path,
experiment='visual-SSVEP',
site='eegnb_examples')
subject = 1
session = 1
raw = load_data(eegnb_data_path,
experiment='visual-SSVEP',
site='eegnb_examples',
device='muse2016',
sfreq=256.,
subject_nb=subject,
session_nb=session,
ch_ind=[0, 1, 2, 3, 4],
replace_ch_names={'Right AUX': 'POz'})
###################################################################################################
# Visualize the power spectrum
# ----------------------------
raw.plot_psd()
###################################################################################################
# Epoching
# ----------------------------
