lmer

Perform linear mixed-effects model (lmer) or linear model (lm) comparisons using R's `lme4`, `emmeans` and `nlme` packages.

Parameters

Name	Type	Default	Description
control_group	str	'HC'	The control group to be used for comparisons
data_col	str	'Y'	The name of the data column to be analyzed
other_col	list	['ID', 'Group', 'Epoch', 'Sensor']	The names of other columns to include in the analysis
data_index	int	-1	The index of the data column to be analyzed (-1 for entire column)
models	dict	None	Dictionary of model formulas (keys are model names)
bic_models	list	None	List of models to evaluate using BIC
anova_tests	dict	None	Dictionary specifying models for ANOVA tests
specs	string	None	Specifications for emmeans function in R

Returns

dict: A dictionary containing analysis results (keys are group comparison names, values are DataFrames)

cohend

Compute Cohen's d for all pairwise group comparisons across sensors.

Parameters

Name	Type	Default	Description
control_group	str	'HC'	The control group to be used for comparisons
data_col	str	'Y'	The name of the data column to be analyzed
data_index	int	-1	The index of the data column to be analyzed (-1 for entire column)

Returns

dict: A dictionary containing Cohen's d values (keys are group comparison names, values are DataFrames)

EEG_topographic_plot

Generate a topographical plot of EEG data using the 10-20 electrode placement system.

Parameters

Name	Type	Default	Description
radius	float	0.6	Radius of the head circumference
pos	float	0.0	Position of the head on the x-axis
electrode_size	float	0.9	Size of the electrodes
label	bool	True	Show the colorbar label
ax	matplotlib Axes	None	Axes object to plot the data
fig	matplotlib Figure	None	Figure object to plot the data
vmin	float	None	Min value used for plotting
vmax	float	None	Max value used for plotting

catch22

Compute the catch22 features from a time-series sample.

Parameters

Name	Type	Description
sample	np.array	Sample data

Returns

np.array: Array with the catch22 feature values

power_spectrum_parameterization

Power spectrum parameterization of a time-series sample using the FOOOF algorithm.

Parameters

Name	Type	Default	Description
sample	np.array		Time-series sample
fs	float		Sampling frequency
fmin	float		Minimum frequency for power spectrum fit
fmax	float		Maximum frequency for power spectrum fit
fooof_setup	dict		Parameters for FOOOF algorithm
r_squared_th	float	0.9	Threshold for r_squared value
freq_range	list	[30., 200.]	Frequency range for peak search
nperseg	int	-1	Length of each segment for power spectrum
compute_knee	bool	False	Whether to compute knee parameter

Returns

np.array: Array with aperiodic components, peak parameters, and mean power

fEI

Calculates fEI (on a set window size) for a signal.

Parameters

Name	Type	Description
signal	array	Amplitude envelope for all channels
sampling_frequency	int	Sampling frequency
window_size_sec	float	Window size in seconds
window_overlap	float	Fraction of overlap between windows (0-1)
DFA_array	array	Array of DFA values for thresholding
bad_idxes	array	Channels to ignore from computation

Returns

tuple: (fEI_outliers_removed, fEI_val, num_outliers, wAmp, wDNF)

DFA

Calculates DFA (Detrended Fluctuation Analysis) of a signal.

Parameters

Name	Type	Description
signal	array	Amplitude envelope for all channels
sampling_frequency	int	Sampling frequency
fit_interval	list	Interval (seconds) for DFA exponent fit
compute_interval	list	Interval (seconds) for DFA computation
overlap	bool	Whether to use 50% window overlap
bad_idxes	array	Channels to ignore from computation

Returns

tuple: (dfa_array, window_sizes, fluctuations, dfa_intercept)

hctsa

Compute hctsa (Highly Comparative Time-Series Analysis) features.

Parameters

Name	Type	Description
samples	ndarray/list	Time-series samples
hctsa_folder	str	Folder where hctsa is installed
workers	int	Number of MATLAB workers

Returns

list: hctsa features (n_samples × n_features)

compute_features

Compute features from the data using the specified method.

Parameters

Name	Type	Description
data	pd.DataFrame	Data containing time-series samples in 'Data' column
hctsa_folder	str	Folder where hctsa is installed (if method is 'hctsa')

Returns

pd.DataFrame: Original data with features appended

create_kernel

Create kernels from multicompartment neuron network descriptions.

Parameters

Name	Type	Description
MC_folder	str	Path to the folder containing the multicompartment neuron network descriptions.
output_path	str	Path to the output folder.
params	module	Network parameters module.
biophys	list	List of biophysical membrane properties.
dt	float	Time step.
tstop	float	Simulation time.
electrodeParameters	dict	Electrode parameters. If None, no LFP is computed.
CDM	bool	Compute the current dipole moment.

Returns

dict: Dictionary containing the kernels.

compute_MEEG

Compute EEG/MEG from a current dipole moment using the NYHeadModel or a homogeneous volume conductor model.

Parameters

Name	Type	Description
CDM	np.array	Current dipole moment.
location	np.array	Location of the dipole moment.
cMEG	bool	Compute MEG instead of EEG.

Returns

list: MEEG at the electrode/sensor locations.

compute_proxy

Compute a proxy for the extracellular signal by combining variables directly measured from network simulations.

Parameters

Name	Type	Description
method	str	Method to compute the proxy (e.g., 'FR', 'AMPA', 'GABA').
sim_data	dict	Dictionary containing the simulation data.
sim_step	float	Simulation time step in ms.

Returns

np.array: Proxy for the extracellular signal.

add_simulation_data

Add features and parameters to the training data.

Parameters

Name	Type	Description
features	np.ndarray	Features.
parameters	np.ndarray	Parameters to infer.

initialize_sbi

Initialize the SBI inference method (NPE, NLE, or NRE) using the appropriate neural estimator.

Parameters

Name	Type	Description
hyperparams	dict	Dictionary of hyperparameters required to set up the inference method.

Returns

object: Configured SBI inference object ready for appending simulations and training.

train

Train the model using either SBI or sklearn depending on configuration.

Parameters

Name	Type	Default	Description
param_grid	list	None	List of dictionaries of hyperparameters to search over.
n_splits	int	10	Number of splits for RepeatedKFold cross-validation.
n_repeats	int	10	Number of repeats for RepeatedKFold cross-validation.
train_params	dict	{'learning_rate': 0.0005, 'training_batch_size': 256}	Dictionary of training parameters for SBI.

predict

Predict parameters for new input features.

Parameters

Name	Type	Description
features	np.ndarray	Features to predict parameters for.

Returns

list: List of predictions.

sample_posterior

Sample from the posterior distribution for a given observation.

Parameters

Name	Type	Default	Description
x	np.ndarray		Observed feature vector (1D array).
num_samples	int	10000	Number of posterior samples to draw.

Returns

np.ndarray: Array of posterior samples.

network

Run a network simulation script with the specified parameters.

Parameters

Name	Type	Description
script_path	str	Name of the Python script to run (relative to python_folder).
param_path	str	Name of the parameter file to use (relative to param_folder).

simulate

Run a simulation script with the specified parameters.

Parameters

Name	Type	Description
script_path	str	Name of the Python script to run (relative to python_folder).
param_path	str	Name of the parameter file to use (relative to param_folder).

analysis

Run an analysis script with the specified parameters.

Parameters

Name	Type	Description
script_path	str	Name of the Python script to run (relative to python_folder).
param_path	str	Name of the parameter file to use (relative to param_folder).