# 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 # ----------------------------