sab_dataset module¶

class sab_dataset.ChannelInfo(channel_names)¶

Bases: object

Contains informations about channels

Attributes:

n_chan : int Number of channels chan_names : numpy array (str) Array containing the channels’ name chan_names_no_eeg : numpy array (str) Channel names without the ‘EEG’ prefix chan_elec_names : numpy array (str) Name of the electrode for each channel elec_names : numpy array (str) Name of all different electrodes elec_eeg_names : numpy array (str) Name of all different EEG electrodes n_elec : int Number of electrodes # n_elec_eeg : int Number of EEG electrodes

Methods

get_channel_name(chan_sel_pos)	Get the channel name from the channel’s position
get_channel_pos(chan_sel_name[, true_match])	Return the channel position from the channel’s name
get_eeg_channels()	Used to find the EEG channels
get_electrode_channels_pos(elec_desc)	Get the channels position of the selected electrode
get_electrode_names()	Used to get electrode names from the channel names

get_channel_name(chan_sel_pos)¶

Get the channel name from the channel’s position

Parameters:	chan_sel_pos : int | array Channel’s position
Returns:	chan_names : str | list Channel’s names

get_channel_pos(chan_sel_name, true_match=True)¶

Return the channel position from the channel’s name

chan_sel_name : str

Channel’s name

true_match : bool (default: True)

If True, search an exact match between chan_sel_name parameter and chan_names attribute. If False returns, every channel position containing chan_sel_name

get_eeg_channels()¶

Used to find the EEG channels

Note

EEG channels’ name should start with the prefix ‘EEG’

Note

If no EEG channel can be found, all channels are considered to be EEG channels

Returns:	eeg_channels_ind : array [boolean] Boolean array containing 1 if channel is an EEG channel, 0 otherwise n_eeg_channels : int Number of eeg channels

get_electrode_channels_pos(elec_desc)¶

Get the channels position of the selected electrode

Parameters:	elec_desc : int | str | array Either the number of the electrode, or the name of the electrode to select
Returns:	elec_chan_pos : array Position of the channels of the selected electrode

get_electrode_names()¶

Used to get electrode names from the channel names

Returns:	Electrode names : array

class sab_dataset.SabDataset(matlab_dataset_dirpath, subject_id, dataset_type='rec', colors_dict=[], **kwargs)¶

Bases: object

Class representing a SAB dataset - can represent the encoding phase or recognition phase. SabDataset instances are created from the Matlab EEGrec/EEGenc datasets (See documentation for more details)

Attributes:

matlab_dataset_dirpath : str Path of the Matlab dataset subject_id : str Subject identifier dataset_type : str either ‘rec’ or ‘enc’ for recognition and encoding datasets respectively srate : int Sampling Rate (Hz) data : numpy array [3D] Data matrix 3D - size [n_chan, n_pnts, n_trials] times : numpy array time vector n_chan, n_pnts, n_trials : int number of channels / time points / trials tmin, tmax : num minimum / maximum time channel_info : ChannelInfo instance Contains information about the channels For ‘rec’ datasets: hits, correct_rejects, false_alarms, omissions : list | array binary vectors representing each conditions [size: (n_samples)] reaction_times : list | array reaction times vector [size: (n_samples)]

Methods

create_features([chan_sel, electrode_sel, …])	Create a TimeFeatures instance from the current dataset
downsample(decimate_order)	Downsample the data along the time axis.
plot_electrode_erps(elec_desc[, plot_ci, …])	Plot on the same figure the ERPs for each channel of the selected electrode
plot_erp(channel_desc[, plot_ci, plot_hits, …])	Plot the evoked response averaged over trials
plot_erp_subplot(data, ax, color_str, plot_ci)	Internal rountine used by plot_erp method
plot_itpc(channel_desc[, trial_pos, …])	Plot the ITPC, Inter-Trial Phase Coherence
save([dir_path, filename])	Save the SabDataset instance to a pickle file using the pickle module.
save_sig_to_file(chanpos[, trialpos, output_dir])	Save the signal to a file

plot_mean_spectrum

create_features(chan_sel=[], electrode_sel=[], trial_sel=[])¶

Create a TimeFeatures instance from the current dataset

Parameters:

chan_sel : int | str | array | None (default: None) Channel selection to compute the feature only on these channels. If none, select all channels electrode_sel : int | str | array | None (default: None) Electrode selection to compute the feature only on these electrodes. If none, the selection is done based on chan_sel parameter trial_sel : array | None (default: None) Trial selection to compute the features only with these trials. If none, select all trials

Returns:

TimeFeatures : Instance of TimeFeatures TimeFeatures instance generated from the amplitude of the current dataset, given the selected channels .. note:: If both chan_sel and electrode_sel are provided, the selection is only done given the electrode selection

downsample(decimate_order)¶

Downsample the data along the time axis.

Parameters:	decimate_order : int Order of the down-sampling. Sampling frequency will be divided by this number

plot_electrode_erps(elec_desc, plot_ci=0, plot_hits=1, plot_cr=0, plot_omissions=0, plot_fa=0)¶

Plot on the same figure the ERPs for each channel of the selected electrode

Parameters:

elec_desc : str Electrode’s name plot_ci : bool (default: False) If True, plot the confidence interval around the mean plot_hits : bool (default: True) If True, plot the ERP for the ‘Hit’ condition plot_cr : bool (default: False) If True, plot the ERP for the ‘Correct Reject’ condition plot_omissions : bool (default: False) If True, plot the ERP for the ‘Omission’ condition plot_fa : bool (default: False) If True, plot the ERP for the ‘False Alarme’ condition

plot_erp(channel_desc, plot_ci=1, plot_hits=1, plot_cr=1, plot_omissions=0, plot_fa=0, ax=[], colors_dict=[])¶

Plot the evoked response averaged over trials

Parameters:

channel_desc : str | int | array Channel’s name or position plot_ci : bool (default: True) If True, plot the confidence interval around the mean plot_hits : bool (default: True) If True, plot the ERP for the ‘Hit’ condition plot_cr : bool (default: True) If True, plot the ERP for the ‘Correct Reject’ condition plot_omissions : bool (default: False) If True, plot the ERP for the ‘Omission’ condition plot_fa : bool (default: False) If True, plot the ERP for the ‘False Alarme’ condition ax : list | none (default: none) List of axis where to plot the ERP

Returns:

ax_list : list List of axis where to plot the ERP leg_h : array Array of legends handles

plot_erp_subplot(data, ax, color_str, plot_ci)¶

Internal rountine used by plot_erp method

plot_itpc(channel_desc, trial_pos=[], filt_cf=array([ 3., 3.54095194, 4.17944689, 4.93307353, 5.82259211, 6.87250628, 8.11173816, 9.57442501, 11.30085962, 13.33860028, 15.74378087, 18.58265717, 21.93343201, 25.88840958, 30.55653809, 36.06641099, 42.56980938, 50.24588311, 59.30608584, 70. ]), filt_bw=array([ 1.5, 1.71908979, 1.9701798, 2.25794399, 2.58773897, 2.96570377, 3.39887404, 3.89531311, 4.464262, 5.11631148, 5.86359922, 6.7200357, 7.70156317, 8.82645241, 10.11564282, 11.59313219, 13.28642346, 15.22703661, 17.45109544, 20. ]), f_tolerance=[], noise_tolerance=[], n_monte_carlo=20, ftype='elliptic', forder=4, do_plot=1, contour_plot=1, n_contours=20)¶

Plot the ITPC, Inter-Trial Phase Coherence

Parameters:

channel_desc : int | str | array Channel’s position or name trial_pos : array | None (default: None) Trial to select before computing the ITPC. If none, select all trials filt_cf : array Filter’s center frequency for computing the ITPC (in Hz) filt_bw : array | int Filter’s bandwidth. Can be fixed or evolve with the center frequency. (In Hz) f_tolerance : float | array | None (default: none) Tolerance of the cut-off frequencies. A random number in the interval [-f_tolerance/2, +f_tolerance/2] will be added to the cut-off frequencies. If none, f_tolerance is set to filt_bw / 100 for each filter. noise_tolerance : float | None (default: none) Random noise from a uniform distribution in [-noise_tolerance/2, +noise_tolerance/2] will be added to the signal x_raw. If none, noise_tolerance is set to np.std(data_sel) / 30. n_monte_carlo : int Number of monte carlo repetition ftype : str Filter type: ‘butter’ : Butterworth filter ‘elliptic’ (default) : Elliptic filter ‘fir’ : Finite Impulse Response filter forder : int Filter order - default : 4 do_plot : int | bool If 1, plot the ITPC (default: 0) contour_plot : int | bool If 1 , plot the contours (contourf function) else use the imshow function (default: 1) n_contours : int If contour_plot=1, number of levels in contourf function

See also

phase_utils.itpc

plot_mean_spectrum(channel_desc, fmin=2, fmax=90, plot_hits=1, plot_cr=1, plot_omissions=0, plot_fa=0)¶

save(dir_path='.', filename=[])¶

Save the SabDataset instance to a pickle file using the pickle module.

Parameters:	dir_path : str Path of the directory where it will be saved filename : str Filename

save_sig_to_file(chanpos, trialpos=[], output_dir='.')¶

Save the signal to a file

Parameters:	chanpos : int | str Channel position trialpos : int | None (default: None) Trial position. If none, will save all the trials in a 3D data matrix. output_dir : str (default: ‘.’) Output directory path

sab_dataset.load_sab_dataset(filepath)¶

Load a time SabDataset instance previously saved using the pickle module

Parameters:	filepath : str Path of the pickle file
Returns:	sab_dataset : SabDataset instance The SabDataset instance