CorrCA: Correlated Component Analysis

This repository contains a implementation of Correlated Component Analysis (CorrCA) based on the original Matlab code from Parra's lab.

Usage

Example script demonstrating how to compute CorrCA on EEG evoked data.

import numpy as np
from corrca import CorrCA

# Load your preprocessed EEG data as a NumPy array
# epochs: shape (n_epochs, n_channels, n_times)
# times: shape (n_times,)
epochs = np.load('path/to/your/epochs.npy')
times = np.load('path/to/your/times.npy')

# Define CorrCA parameters
params = {'baseline_window': (-0.3, -0.05), 'response_window': (0., 0.6), 'gamma': 0, 'K': 60, 'stats': True, 'n_surrogates': 500, 'alpha': 0.01}

# Perform CorrCA
W, ISC, A, Y, Yfull, ISC_thr = CorrCA.calc_corrca(epochs, times, **params)

For other use cases look inside calc_corrca() to see how the main functions are called.