Installation and Usage

Install

pip install ieegprep

Usage

After installing, the library can be used directly to:

Read EDF, BrainVision or MEF3 data

from ieegprep import IeegDataReader

# initialize a reader for a specific dataset
reader = IeegDataReader('/bids_data_root/subj-01/ieeg/sub-01_run-06_ieeg.vhdr', preload_data=False)

# print metadata
print(reader.sampling_rate)
print(reader.channel_names)

# retrieve all the data from single channel
channel_data      = reader.retrieve_channel_data('CH05')

# retrieve a range of sample-data from all, a single or multiple channels
range_data_all    = reader.retrieve_sample_range_data(sample_start=1000, sample_end=2000)
range_data_single = reader.retrieve_sample_range_data(sample_start=1000, sample_end=2000, channels='CH01')
range_data_multi  = reader.retrieve_sample_range_data(sample_start=1000, sample_end=2000, channels=('CH01', 'CH05'))

# optionally, close the reader and release the memory
reader.close()

Read BIDS sidecar metadata files

from ieegprep import load_event_info, load_channel_info

channels = load_channel_info('/bids_data_root/subj-01/ieeg/sub-01_run-06_channels.tsv')
events   = load_event_info('/bids_data_root/subj-01/ieeg/sub-01_run-06_events.tsv')

Load, pre-process and epoched data as a matrix

from ieegprep import load_data_epochs

# retrieve epoched data
[srate, epochs] = load_data_epochs( '/bids_data_root/subj-01/ieeg/sub-01_run-06_ieeg.vhdr',
                                    retrieve_channels = channels['name'],
                                    onsets            = events['onset'])
data_ch0_trial13 = epochs[1, 13, :]

# retrieve epoched data specifying the epoch window and baseline normalization
[srate, epochs] = load_data_epochs( '/bids_data_root/subj-01/ieeg/sub-01_run-06_ieeg.vhdr',
                                    retrieve_channels = channels['name'],
                                    onsets            = events['onset'],
                                    trial_epoch       = (-1, 2),                         #  -1s < onset < 2s  
                                    baseline_norm     = 'Median',
                                    baseline_epoch    = (-1, -0.1))
                            
# retrieve epoched data with pre-processing (high-pass filtering, CAR re-referencing and 50Hz line-noise removal)
from ieegprep import RerefStruct
[srate, epochs] = load_data_epochs( '/bids_data_root/subj-01/ieeg/sub-01_run-06_ieeg.vhdr',
                                    retrieve_channels  = channels['name'],
                                    onsets             = events['onset'],
                                    high_pass          = True,
                                    early_reref        = RerefStruct.generate_car(channels['name'].tolist()),
                                    line_noise_removal = 50)

Load, epoch and get the average of each stimulated electrode pair (minimizing memory usage)

from ieegprep import load_data_epochs_averages, load_elec_stim_events

# load the events file and retrieve the different stimulated electrode-pairs (e.g. Ch01-Ch02...) as conditions
_, _, conditions_onsets, _ = load_elec_stim_events('/bids_data_root/subj-01/ieeg/sub-01_run-06_events.tsv')

# retrieve epoch data averaged over conditions
[srate, epochs, _] =  load_data_epochs_averages('/bids_data_root/subj-01/ieeg/sub-01_run-06_ieeg.vhdr',
                                                retrieve_channels = channels['name'],
                                                conditions_onsets = conditions_onsets)
data_respCh01_stimCh02_03 = epochs[1, 4, :]