Input arguments

Input file paths

Argument	Description	Python example	R example
vhdr_files (required)	List of raw EEG file paths	['raw/Vp01.vhdr', 'raw/Vp02.vhdr', ...]	c("raw/Vp01.vhdr", "raw/Vp02.vhdr", ...)
	Nested list if (some) participants have multiple EEG files or	['raw/Vp01.vhdr', ['raw/Vp02_a.vhdr', 'raw/Vp02_b.vhdr'], ...]	list("raw/Vp01.vhdr", c("raw/Vp02_a.vhdr", "raw/Vp02_b.vhdr"), ...)
	Directory path with raw EEG files	'raw'	"raw"
log_files (required)	List of behavioral log file paths	['log/Vp01.txt', 'log/Vp02.txt', ...]	c("log/Vp01.txt", "log/Vp02.txt", ...)
	Directory of raw EEG files	'log'	"log"

Output file paths

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

Preprocessing options

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

Epoching options

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

Averaging options

Argument	Description	Python example	R 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', ...}	list(neg_unrel = "context == 'negative' & semantics == 'unrelated' & rt < 3000", ...)

Time-frequency analsis options

Argument	Description	Python example	R example
perform_tfr (default: False)	Enable time-frequency analysis or	True or False	TRUE or FALSE
tfr_subtract_evoked (default: False)	Subtract evoked activity from epochs before time-frequency analysis or	True or False	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)	seq(6.0, 40.0, by = 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)	seq(2.0, 20.0, by = 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	"percent" or "ratio" or "logratio" or "zscore" or "zlogratio" or NULL
tfr_baseline (default: (-0.45, -0.05))	Time window for subtractive baseline correction of event-related power	(-0.45, -0.05)	c(-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', ...]]}	list(name = list("alpha"), tmin = list(0.05), tmax = list(0.25), fmin = list(8.0), fmax = list(13.0), roi = list(c("PO9", ...)))

Permutation test options

Argument	Description	Python example	R example
perm_contrasts (default: None)	Contrast(s) between condition labels (see average_by) to compute cluster-based permutation tests for	[('related', 'unrelated')]	list(c("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)	0.0 or NULL (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)	1.0 or NULL (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)	c("C1", "Cz", "C2", ...) or NULL (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)	8.0 or NULL (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)	30.0 or NULL (i.e., use all frequencies)

Performance options

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