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: spectral_component_analysis_dft.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] = spectral_component_analysis_dft(x,fl,fu)
% spectral_component_analysis_dft - Spectral component analysis (SCA) using
% a Fourier domain approach. Extracts and ranks linear mixtures of
% multichannel data with maximal energy in a given frequency band.
%
% Usage:
%   [y,W,A] = spectral_component_analysis_dft(x,fl,fu)
%
% Inputs:
%   x: Input data array (channels x samples).
%   fl: lower cutoff frequency normalized by sampling frequency
%   fu: upper cutoff frequency normalized by sampling frequency
%
% Outputs:
%   y: Extracted spectral components ranked by their energy in the
%       frequency band of interest
%   W: Extraction (separation) matrix
%   A: Mixing matrix
%
% Reference: - Sameni, R., Jutten, C., & Shamsollahi, M. B. (2009). A
%   deflation procedure for subspace decomposition. IEEE Transactions on
%   Signal Processing, 58(4), 2363-2374.
%
% Revision History:
%   2009: First release
%   2023: Renamed from deprecated version SCA. corrected a frequency
%       binning bug
%
% Reza Sameni, 2009-2023 The Open-Source Electrophysiological Toolbox
% https://github.com/alphanumericslab/OSET

L2 = size(x,2);

freq_indexes = max(round(L2*fl), 1) : min(round(L2*fu), L2); % indexes of the frequency range of interest (half band only)

    
function [y,W,A] = spectral_component_analysis_dft(x,fl,fu)
% spectral_component_analysis_dft - Spectral component analysis (SCA) using
% a Fourier domain approach. Extracts and ranks linear mixtures of
% multichannel data with maximal energy in a given frequency band.
%
% Usage:
%   [y,W,A] = spectral_component_analysis_dft(x,fl,fu)
%
% Inputs:
%   x: Input data array (channels x samples).
%   fl: lower cutoff frequency normalized by sampling frequency
%   fu: upper cutoff frequency normalized by sampling frequency
%
% Outputs:
%   y: Extracted spectral components ranked by their energy in the
%       frequency band of interest
%   W: Extraction (separation) matrix
%   A: Mixing matrix
%
% Reference: - Sameni, R., Jutten, C., & Shamsollahi, M. B. (2009). A
%   deflation procedure for subspace decomposition. IEEE Transactions on
%   Signal Processing, 58(4), 2363-2374.
%
% Revision History:
%   2009: First release
%   2023: Renamed from deprecated version SCA. corrected a frequency
%       binning bug
%
% Reza Sameni, 2009-2023 The Open-Source Electrophysiological Toolbox
% https://github.com/alphanumericslab/OSET

L2 = size(x,2);

freq_indexes = max(round(L2*fl), 1) : min(round(L2*fu), L2); % indexes of the frequency range of interest (half band only)

X = fft(x, L2, 2); % Fourier transform of the input data
Bf = X(:, freq_indexes) * X(:, freq_indexes)'; % Compute the spectral covariance matrix
B = 2 * real(Bf); % Multiply by 2 to compensate for counting only half the signal power in the frequency range of interest
B = (B + B') / 2; % Make the matrix symmetric

C = x * x'; % Compute the covariance matrix
C = (C + C') / 2; % Make the matrix symmetric

[V, D] = eig(B, C, 'chol'); % Perform eigenvalue decomposition using Cholesky decomposition

[~, II] = sort(diag(D), 'descend