You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: nonstationary_component_analysis.m
    path of file: ./repos/OSET/matlab/tools/bss
    
    the code of the file until where you have to start completion: function [y, W, A, B, C, lambda] = nonstationary_component_analysis(x, I, J, varargin)
% nonstationary_component_analysis - Nonstationary Component Analysis (NSCA)
%   algorithm to extract temporal nonstationarities from multichannel data
%
% Usage:
%   [y, W, A, B, C, lambda] = nonstationary_component_analysis(x, I, J, mode)
%
% Inputs:
%   x: Mixture of signals, where each row represents a different signal (N x T)
%   I: Desired time window indices (in 1:T)
%   J: Desired time window indices (in 1:T)
%   mode (optional): Calculate covariance ('COV'), correlation ('CORR') or

    
function [y, W, A, B, C, lambda] = nonstationary_component_analysis(x, I, J, varargin)
% nonstationary_component_analysis - Nonstationary Component Analysis (NSCA)
%   algorithm to extract temporal nonstationarities from multichannel data
%
% Usage:
%   [y, W, A, B, C, lambda] = nonstationary_component_analysis(x, I, J, mode)
%
% Inputs:
%   x: Mixture of signals, where each row represents a different signal (N x T)
%   I: Desired time window indices (in 1:T)
%   J: Desired time window indices (in 1:T)
%   mode (optional): Calculate covariance ('COV'), correlation ('CORR') or
%       symmetrized covariance ('SYMM-COV') matrices. Default: 'COV'
%
% Outputs:
%   y: Separated components of the input signals (N x T).
%   W: Separation filters.
%   A: Inverse of separation filters.
%   B: Covariance matrix computed from time window I.
%   C: Covariance matrix computed from time window J.
%   lambda: generalized eigenvalues sorted in descend