How to get rid of the unknown errors?

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I am trying to run a code on EEG waves by importing a dataset in edf file.

To run this code in Matlab you would require to download the function edffile in Matlab. Then run the code. This code is required to extract band of EEG frequencies, and we have obtained the error as mentioned below:

The length of the segments cannot be greater than the length of the input signal.

Error in welchparse (line 32)

[L,noverlap,win] = segment_info(M,win,noverlap);

Error in spectrogram (line 172)

[x,nx,~,~,~,win,~,~,noverlap,~,~,options] = welchparse(x,esttype,varargin{:});"

to download the function to read edf file and to run the program i have also provided the link below

https://blogs.mathworks.com/pick/2018/10/12/edf-read/?s_tid=srchtitle you can download this

And to download the dataset the link given below:

https://blogs.mathworks.com/pick/2018/10/12/edf-read/?s_tid=srchtitle

to run the code you need to download one subject and get the output.

[Header,S1] = edfread('Subject12_1.edf');
  %Extract Channel O2 and perform epoching to get 1280 samples
data=S1;
                            % loading 1-channel data
Sampling_time_period=2;                             % sampling time period
sampling_frequency=500;                             % sampling frequency
[n,m]=size(data);                                   % obtain size of data
t=(1:n)*Sampling_time_period;                       % generates time vector
h=figure
plot(t,data,'p');
title('EEG Data')
grid on                                             % plot with grid
h1=figure
plot(t,data(:,1), 'b-')                             % plot without grid
%-----------------------------------------------------------------------%
%POWER SPECTRUM USING FFT
%-----------------------------------------------------------------------%
y=fft(data);                                        % fast fourier transform
power_spectrum=abs(y).^2;                           % power spectrum using fft
frequency=(1:n)*sampling_frequency/n;               % frequency vector
h2=figure
plot(frequency,20*log(power_spectrum),'b')          % plotting frequency
title('Power spectrum using fft')
%------------------------------------------------------%
%SPECTROGRAM of channel 1
%------------------------------------------------------%
[S1,F,T] = spectrogram(data(:,1),chebwin(128,100),0,sampling_frequency);
%--------------------------------------------------------------------------------%
%w=chebwin(L,r)returns the column vector w containing length L chebyshev window ;
%r is sidelobe attenuation ;
%gives 128 point chebyshev window. r = 100 dB here
%--------------------------------------------------------------------------------%
S1=abs(S1);
h4=figure
mesh(T,F,S1);
xlabel('Time (sec)','FontSize',14);
ylabel('Frequency (Hz)','FontSize',14);
zlabel('S1','FontSize',14);
%-------------------------------------------------------------------------%
%FREQUENCY AND SINGLE SIDED AMPLITUDE SPECTRUMS OF APLHA BETA GAMA DELTA
%-------------------------------------------------------------------------%
%-------------------------------------------------------------------------%
%DELTA BAND-PASS FILTER (0-4)
%-------------------------------------------------------------------------%
sampling_frequency = 500;               % Sampling Frequency
Fpass = 0;                              % Passband Frequency
Fstop = 4;                              % Stopband Frequency
Dpass = 0.057501127785;                 % Passband Ripple
Dstop = 0.0001;                         % Stopband Attenuation
density  = 20;                          % Density Factor
% Calculating the order from the parameters using FIRPMORD.
[n, fo, ao, w] = firpmord([Fpass, Fstop]/(sampling_frequency/2), [1 0], [Dpass, Dstop]); 
% fo is the frequency ao is the amplitude response vector
% Calculating the coefficients using the FIRPM function.
b1 = firpm(n, fo, ao, w, {density});
Hd1 = dfilt.dffir(b1);                                 %discrete time direct form fir filter
x1=filter(Hd1,data);
h9=figure
plot(t,x1,'k')
title('waveform for DELTA band')
%================================================================%
%FREQUENCY SPECTRUM OF DELTA
%================================================================%
L=10;
sampling_frequency=500;
N_point_fft = 2^nextpow2(L);                           % Next power of 2 from length of x1
y1 = fft(x1,N_point_fft)/L;                            % for N point fft
f = sampling_frequency/2*linspace(0,1,N_point_fft/2);  % N_point_fft/2 points b/w 0 and 1
%================================================================%
%SINGLE SIDED AMPLITUDE SPECTRUM
%================================================================%
h10=figure
plot(f,2*abs(y1(1:N_point_fft/2))) 
title('Single-Sided Amplitude Spectrum of DELTA x1(t)')
xlabel('Frequency (Hz)')
ylabel('|Y1(f)|')
%-----------------------------------------------------------------%
%THETA- BAND PASS FILTER (4-7)
%-----------------------------------------------------------------%
sampling_frequency = 500; % Sampling Frequency
Fstop1 = 3.5;             % First Stopband Frequency
Fpass1 = 4;               % First Passband Frequency
Fpass2 = 7;               % Second Passband Frequency
Fstop2 = 7.5;             % Second Stopband Frequency
Dstop1 = 0.0001;           % First Stopband Attenuation
Dpass  = 0.057501127785;  % Passband Ripple
Dstop2 = 0.0001;          % Second Stopband Attenuation
density   = 20;           % Density Factor
% Calculating the order from the parameters using FIRPMORD.
[n, fo, ao, w] = firpmord([Fstop1 Fpass1 Fpass2 Fstop2]/(sampling_frequency/2), [0 1 0], [Dstop1 Dpass Dstop2]);
% fo is the frequency ao is the amplitude response vector
% Calculating the coefficients using the FIRPM function.
b2 = firpm(n, fo, ao, w, {density});
Hd2 = dfilt.dffir(b2);                                  %discrete time direct form fir filter
x2=filter(Hd2,data);
h11=figure
plot(t,x2,'r')
title('waveform for THETA band')
%===================================================%
%FREQUENCY SPECTRUM OF THETA 
%===================================================%
L=10;
sampling_frequency=500;
N_point_fft = 2^nextpow2(L);                            % Next power of 2 from length of x2
y2 = fft(x2,N_point_fft)/L;                             % for N point fft
f = sampling_frequency/2*linspace(0,1,N_point_fft/2);   % N_point_fft/2 points b/w 0 and 1
%===================================================%
%SINGLE SIDED AMPLITUDE SPECTRUM 
%===================================================%
h12=figure
plot(f,2*abs(y2(1:N_point_fft/2))) 
title('Single-Sided Amplitude Spectrum of THETA x2(t)')
xlabel('Frequency (Hz)')
ylabel('|Y2(f)|')
%----------------------------------------------------%
%ALPHA BAND PASS FILTER (8-12)
%----------------------------------------------------%
sampling_frequency = 500; % Sampling Frequency
Fstop1 = 7.5;             % First Stopband Frequency
Fpass1 = 8;               % First Passband Frequency
Fpass2 = 12;              % Second Passband Frequency
Fstop2 = 12.5;            % Second Stopband Frequency
Dstop1 = 0.0001;          % First Stopband Attenuation
Dpass  = 0.057501127785;  % Passband Ripple
Dstop2 = 0.0001;          % Second Stopband Attenuation
density   = 20;           % Density Factor
% Calculating the order from the parameters using FIRPMORD.
[n, fo, ao, w] = firpmord([Fstop1 Fpass1 Fpass2 Fstop2]/(sampling_frequency/2), [0 1 0], [Dstop1 Dpass Dstop2]);
% fo is the frequency ao is the amplitude response vector
% Calculating the coefficients using the FIRPM function.
b3  = firpm(n, fo, ao, w, {density});
Hd3 = dfilt.dffir(b3);                                  %discrete time direct form fir filter
x3=filter(Hd3,data);
h13=figure
plot(t,x3,'g')
title('waveform for ALPHA band')
%=========================================================%
%FREQUENCY SPECTRUM OF ALPHA BAND
%=========================================================%
L=10;
sampling_frequency=500;
N_point_fft = 2^nextpow2(L);                            % Next power of 2 from length of x3
y3 = fft(x3,N_point_fft)/L;                             % for N point fft
f = sampling_frequency/2*linspace(0,1,N_point_fft/2);   % N_point_fft/2 points b/w 0 and 1
%==========================================================%
%SINGLE SIDED AMPLITUDE SPECTRUM
%==========================================================%
h14=figure
plot(f,2*abs(y3(1:N_point_fft/2))) 
title('Single-Sided Amplitude Spectrum of ALPHA x3(t)')
xlabel('Frequency (Hz)')
ylabel('|Y3(f)|')
%------------------------------------------------%
%BETA  BAND PASS FILTER (12-30)
%------------------------------------------------%
sampling_frequency = 500; % Sampling Frequency
Fstop1 = 11.5;            % First Stopband Frequency
Fpass1 = 12;              % First Passband Frequency
Fpass2 = 30;              % Second Passband Frequency
Fstop2 = 30.5;            % Second Stopband Frequency
Dstop1 = 0.0001;          % First Stopband Attenuation
Dpass  = 0.057501127785;  % Passband Ripple
Dstop2 = 0.0001;          % Second Stopband Attenuation
density   = 20;           % Density Factor
% Calculating the order from the parameters using FIRPMORD.
[n, fo, ao, w] = firpmord([Fstop1 Fpass1 Fpass2 Fstop2]/(sampling_frequency/2), [0 1 0], [Dstop1 Dpass Dstop2]);
% fo is the frequency ao is the amplitude response vector
% Calculatng the coefficients using the FIRPM function
b4   = firpm(n, fo, ao, w, {density});
Hd4 = dfilt.dffir(b4);                                  %discrete time direct form fir filter
x4=filter(Hd4,data);
h15=figure
plot(t,x4,'y')
title('waveform for BETA band')
%==========================================================%
%FREQUENCY SPECTRUM OF BETA
%==========================================================%
L=10;
sampling_frequency=500;
N_point_fft = 2^nextpow2(L);                            % Next power of 2 from length of x4
y4 = fft(x4,N_point_fft)/L;                             % for N point fft
f = sampling_frequency/2*linspace(0,1,N_point_fft/2);   % N_point_fft/2 points b/w 0 and 1
%==========================================================%
%SINGLE SIDED AMPLITUDE SPECTRUM
%==========================================================%
h16=figure
plot(f,2*abs(y4(1:N_point_fft/2))) 
title('Single-Sided Amplitude Spectrum of BETA x4(t)')
xlabel('Frequency (Hz)')
ylabel('|Y4(f)|')
%============================================================%
%AMPLITUDE SPECTRUMS TOGETHER
%============================================================%
h17=figure
plot(f,2*abs(y1(1:N_point_fft/2)),'k',f,2*abs(y2(1:N_point_fft/2)),'r',f,2*abs(y3(1:N_point_fft/2)),'g',f,2*abs(y4(1:N_point_fft/2)),'b')
title('Single-Sided Amplitude Spectrums')
xlabel('Frequency (Hz)')
ylabel('|Y(f)|')
legend('DELTA','THETA','APLHA','BETA');