Pipeline inputs

Input file paths

Argument	Description	Example
vhdr_files (required)	List of raw EEG file paths	['raw/Vp01.vhdr', 'raw/Vp02.vhdr', ...]
	Nested list if (some) participants have multiple EEG files or	[['raw/Vp01_a.vhdr', 'raw/Vp01_b.vhdr'], 'raw/Vp02.vhdr', ...]
	Directory path with raw EEG files	'raw'
log_files (required)	List of behavioral log file paths	['log/Vp01.txt', 'log/Vp02.txt', ...]
	Directory of raw EEG files	'log'

Output file paths

Argument	Description	Example
output_dir (required)	Output directory	'data/output'
clean_dir (default: None)	Cleaned (continuous) data output directory	'data/clean'
epochs_dir (default: None)	Epoched data output directory	'data/clean'
report_dir (default: None)	HTML quality control report output directory	'data/reports'
to_df (default: True)	Save outputs as data frames with comma-separated values or	True
	Save outputs as MNE-Python (.fif) files or	False
	Save outputs as data frames and MNE-Python files	'both'

Preprocessing options

Argument	Description	Example
downsample_sfreq (default: None)	Downsample to lower sampling rate or	250.0
	Do not downsample	None
veog_channels (default: 'auto')	Construct bipolar VEOG from two EEG or EOG channels or	['Fp1', 'IO1']
	Construct VEOG from default channels or	'auto'
	Do not construct a new VEOG channel	None
heog_channels (default: 'auto')	Construct bipolar HEOG from two EEG or EOG channels or	['F9', 'F10']
	Construct HEOG from default channels or	'auto'
	Do not construct a new HEOG channel	None
montage (default: 'easycap-M1')	Standard EEG montage name or	'easycap-M1'
	Custom EEG montage file path	'data/chanlocs.elc'
ref_channels (default: 'average')	List of channels to re-reference EEG channels to or	['M1', 'M2']
	Re-reference EEG channels to an average reference or	'average'
	Use the Reference Electrode Standardization Technique (REST)	'REST'
bad_channels (default: None)	Lists of bad channels for each participant or	[['P7', 'Oz'], ['Fp2'], ...]
	Dict with participant labels and their list of bad channels or	{'Vp05': ['PO8', 'O2'], ...}
	Auto-detect bad channels based on standard error across epochs or	'auto'
	Don’t interpolate any bad channels	None
besa_files (default: None)	Directory of BESA/MSEC correction matrix files or	'data/cali'
	List of BESA/MSEC correction matrix file paths or	['data/cali/Vp01.matrix', 'data/cali/Vp02.matrix', ...]
	Don’t use BESA/MSEC ocular correction	None
ica_method (default: None)	ICA method or	'fastica' or 'infomax' or 'picard'
	Don’t apply ICA	None
ica_n_components (default: None)	Number of ICA components to use or	15
	Proportion of variance explained by ICA components or	0.99
	Use (almost) all possible ICA components	None
highpass_freq (default: 0.1)	High-pass filter cutoff frequency or	0.1
	Do not apply high-pass filter	None
lowpass_freq (default: 40.0)	Low-pass filter cutoff frequency or	40.0
	Do not apply low-pass filter	None

Epoching options

Argument	Description	Example
triggers (recommended, default: None)	Numerical EEG triggers for events of interest	[201, 202]
triggers_column (default: None)	Name of log file column with EEG triggers for automatic matching	'trigger'
epochs_tmin (default: -0.5)	Start of epochs relative to event onset (in s)	-0.5
epochs_tmax (default: 1.5)	End of epochs relative to event onset (in s)	1.5
baseline (default: (-0.2, 0.0))	Time window for baseline correction (in s) or	(-0.2, 0.0)
	Use entire prestimulus interval or	(None, 0.0)
	Do not perform baseline correction	None
reject_peak_to_peak (default: 200.0)	Peak-to-peak threshold for rejecting epochs (in µV) or	200.0
	Do not reject epochs based on peak-to-peak amplitude	None
components (recommended, default: None)	Definition of single trial ERP components of interest	{'name': ['P1', 'N170'], 'tmin': [0.08, 0.15], 'tmax': [0.13, 0.2], 'roi': [['PO3', ...], ['P7', ...]]}

Averaging options

Argument	Description	Example
average_by (recommended, default: None)	Selection of (combinations of) conditions to create by-participant averages for (keys = custom condition labels, values = Pandas query)	{'neg_unrel': 'context == "negative" & semantics == "unrelated" & rt < 3000', ...}

Time-frequency analsis options

Argument	Description	Example
perform_tfr (default: False)	Enable time-frequency analysis or	True or False
tfr_subtract_evoked (default: False)	Subtract evoked activity from epochs before time-frequency analysis or	True or False
tfr_freqs (default: np.linspace(4.0, 40.0, num=37))	Frequencies for the family of Morlet wavelets	np.arange(6.0, 41.0, step=2.0)
tfr_cycles (default: np.linspace(2.0, 20.0, num=37))	Numbers of cycles for the family of Morlet wavelets	np.arange(2.0, 21.0, step=1.0)
tfr_mode (default: 'percent')	Method for divisive baseline correction of event-related power using the full epoch interval (Delorme & Grandchamp, 2012)	'percent' or 'ratio' or 'logratio' or 'zscore' or 'zlogratio' or None
tfr_baseline (default: (-0.45, -0.05))	Time window for subtractive baseline correction of event-related power	(-0.45, -0.05)
tfr_components (default: None)	Similar to components, the definition of single trial event-related power bands of interest	{'name': ['alpha'], 'tmin': [0.05], 'tmax': [0.25], 'fmin': [8.0], 'fmax': [13.0], 'roi': [['PO9', ...]]}

Permutation test options

Argument	Description	Example
perm_contrasts (default: None)	Contrast(s) between condition labels (see average_by) to compute cluster-based permutation tests for	[('related', 'unrelated')]
perm_tmin (default: 0.0)	Start of time window (in s relative to stimulus onset) for restricting the permutation test	0.0 or None (i.e., use entire epoch)
perm_tmax (default: 1.0)	End of time window (in s relative to stimulus onset) for restricting the permutation test	1.0 or None (i.e., use entire epoch)
perm_channels (default: None)	Selection of channels for restricting the permutation test	['C1', 'Cz', 'C2', ...] or None (i.e., use all channels)
perm_fmin (default: None)	Lowest frequency (in Hz) for restricting the permutation test (event-related power only)	8.0 or None (i.e., use all frequencies)
perm_fmax (default: None)	Highest frequency (in Hz) for restricting the permutation test (event-related power only)	30.0 or None (i.e., use all frequencies)

Performance options

Argument	Description	Example
n_jobs (default: 1)	Number of jobs (i.e., participants) to be processed in parallel	4 or -1 (i.e., use all CPUs)