BIOM 480A: Biomedical Signal and ImageProcessing
Colorado State University
Student: Minh Anh Nguyen
Email: minhanhnguyen@q.com
Problem
10.3
Import the data in the file
"P-10_3.xls" and plot the EEG. The sample rate of the data was 173.61
Hz.
a. Determine the normal EEG frequency spectrum
b. Plot the power spectrum of the signal
d. Calculated the fractal dimension, signal complexity, signal mobility of
this signal and compare it with the same value in the problem.4.
signal complexity for original data = 1.0286
signal complexity
for before grand mal seizures = 1.0936
signal complexity
for during grand mal seizures = 0.9967
signal complexity
for after grand mal seizures = 1.2358
signal mobility for original data = 1.1102
signal mobility for
before grand mal seizures = 0.6585
signal mobility for
during grand mal seizures = 1.0823
signal mobility for
after grand mal seizures = 0.8971
fractal dimension for original data = 1.875
fractal dimension for before grand mal
seizures = 1.985
fractal dimension for during grand mal seizures
= 1.9952
fractal dimension for after grand mal
seizures = 1.989
The simulation results above showed: 1. the
complexity dropped lower than the mobility for the during grand mal seizures
simulation. 2. The mobility lower than complexity for before and after grand
mal seizures. These results confirmed the definition, which is given in section
6.2.1 of the Biomedical Signal and Image processing textbook, is corrected. The
Fractal dimension, measure of self-similarity of an original data, is 1.8. the
plots above showed the slope of the plot ln(l(k) versus ln(1/k).
For the problem 10.4:
signal complexity = 1.3403
signal mobility = 0.5205
fractal dimension = 1.9559
When we compared the simulation results from
the problem 10.4 with problem 10.3, we saw that the complexity value of the
problem 10.4 is higher than the complexity value of the original data of the
problem 10.3. According to the definition in the section 6.2 and 6.2.1 of the
Biomedical Signal and Image processing textbook, “the normal and healthy
biomedical systems are complex. Once a disease occurs, the complexity of the
system drops” the data provided for the problem 10.4 is collected form a
healthy patient. We did know that the data for problem 10.3 is for grand mal
seizures; so we expected to see the complexity value for this data lower than
the complexity value for problem 10.4.
Problem 6.5
Import and plot the
EEG signal captured under a fixed condition.
a. Calculate the mean of the stochastic process
The
mean is 0.675834148647028
b. Calculate the variance of stochastic process
The
variance is 4.739270596953347e+02
Assume Gaussian distribution; find PDF of the
stochastic process.
Use the Matlab command xcorr, calculate an estimate of the autocorrelation function
e Using
the correlation function estimate in part “c”. Estimate the power spectrum of
the process.
-
The correlation function estimate in part “c”
= 1. We see that the
autocorrelation function is the auto-covariance, P(x=0) = 1.
Do
you see any visible frequency in the power spectrum? Explain.
Yes, from the power spectrum plot above, all
the spurious frequency peaks caused by noise did remove from the plot. The spectral component at 46, 131, 367, and
411 Hz that were buried in noise is now visible. Averaging did remove variance
from the spectrum; as a result, this yields more accurate power measurements.
The plot of the power spectrum shows two
expected peaks at DC, 101, and 146 Hz. It also shows several more spurious
peaks that must be caused by noise in the signal.
Matlab code:
close
all;
clear
all;
clc;
%%
problem 10.3
%%import
the data in the file "P-10_3.xls" and plot the signal
problem10_3
=xlsread('I:\BIOM480A3\HW4\p_10_3.xls');
%
=xlsread('\\tsclient\E\BIOM480A3\HW4\p_10_3.xls');
y1
= problem10_3(:);
fs
= 173.61;% sampling rate or frequency (Hz)
N
= length(y1);% find the length of the data per second
T
= 1/fs % period between each sample
ls
= size(y1); %% size
fs2
= 1/ N;% find the sampling rate or frequency
t
= (0 : N-1) /fs;
t1
= (0 : N-1) *T;%t = (0:1:length(y1)-1)/fs; % sampling period
Nyquist
= fs/2;
figure;
plot(t,y1,'b');
title
('plot of the orignal EEG signal of a grand mal assault')
xlabel
('time (sec)')
ylabel
('Amplitute (uv)')
grid
on;
figure;
y1bef
= y1(5:1800);
N1
= length(y1bef);
tbefore
= (0 : N1-1) /fs;
subplot
(3,1,1);
plot(tbefore,y1bef,'b');
title
('plot of the before Grand Mal seizures ')
xlabel
('time (sec)')
ylabel
('Amplitute (uv)')
grid
on;
y1during
= y1(8000:12000 );
N2
= length(y1during);
tduring
= (0 : N2-1) /fs;
%figure;
subplot
(3,1,2);
plot(tduring,y1during,'b');
title
('plot of the during Grand Mal seizures ')
xlabel
('time (sec)')
ylabel
('Amplitute (uv)')
grid
on;
y1after
= y1(15000:17500 );
N3
= length(y1after);
tafter
= (0 : N3-1) /fs;
%figure;
subplot
(3,1,3);
plot(tafter, y1after,'b');
title
('plot of the after Grand Mal seizures ')
xlabel
('time (sec)')
ylabel
('Amplitute (uv)')
grid
on;
%% Fourier Transform:
X = fftshift(fft(y1));
X1= fftshift(fft(y1bef));
X2= fftshift(fft(y1during));
X3= fftshift(fft(y1after));
%% Frequency specifications:
dF = fs/N; % hertz
f = -fs/2:dF:fs/2-dF; % hertz
%% Frequency specifications:
dF1 = fs/N1; % hertz
f1 = -fs/2:dF1:fs/2-dF1; % hertz
%% Frequency specifications:
dF2 = fs/N2; % hertz
f2 = -fs/2:dF2:fs/2-dF2; % hertz
%% Frequency specifications:
dF3 = fs/N3; % hertz
f3 = -fs/2:dF3:fs/2-dF3; % hertz
%% Plot the spectrum:
figure;
subplot (2,2,1);
plot(f,abs(X)/N);
xlabel('Frequency (in hertz)');
title('Magnitude Response of original');
%% Plot the spectrum: before
%figure;
subplot (2,2,2);
plot(f1,abs(X1)/N1);
xlabel('Frequency (in hertz)');
title('Magnitude Response for before Grand
Mal seizures');
%% Plot the spectrum: during
%figure;
subplot (2,2,3);
plot(f2,abs(X2)/N2);
xlabel('Frequency (in hertz)');
title('Magnitude Response for during Grand
Mal seizures');
%% Plot the spectrum: after
%figure;
subplot (2,2,4);
plot(f3,abs(X3)/N3);
xlabel('Frequency (in hertz)');
title('Magnitude Response for after Grand
Mal seizures');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
xmag
= abs(fft(y1));
binval
= 0:N-1;
faxnom
= (binval*(fs/N))/Nyquist;
N_2
= ceil(N/2);
figure;
plot(faxnom(1:N_2),
xmag(1:N_2))
xlabel({'Frequency
(Normalised to Nyquist Frequency. ' ...
'1=Nyquist frequency)'})
ylabel('Magnitude');
title('Single-sided
Magnitude spectrum for orignal data (Normalised to Nyquist)');
xmagbef
= abs(fft(y1bef));
binvalbef
= 0:N1-1;
faxnombef
= (binvalbef*(fs/N1))/Nyquist;
N_2befo
= ceil(N1/2);
figure;
plot(faxnombef(1:N_2befo),
xmag(1:N_2befo))
xlabel({'Frequency
(Normalised to Nyquist Frequency. ' ...
'1=Nyquist frequency)'})
ylabel('Magnitude');
title('Single-sided
Magnitude spectrum for before Grand Mal seizures(Normalised to Nyquist)');
xmagdr
= abs(fft(y1during));
binvaldr
= 0:N2-1;
faxnomdr
= (binvaldr*(fs/N2))/Nyquist;
N_2dr
= ceil(N2/2);
figure;
plot(faxnomdr(1:N_2dr),
xmag(1:N_2dr))
xlabel({'Frequency
(Normalised to Nyquist Frequency. ' ...
'1=Nyquist frequency)'})
ylabel('Magnitude');
title('Single-sided
Magnitude spectrum for during Grand Mal seizures (Normalised to Nyquist)');
xmagaf1
= abs(fft(y1after));
binvalaf1
= 0:N3-1;
faxnomaf1
= (binvalaf1*(fs/N3))/Nyquist;
N_2af1
= ceil(N3/2);
figure;
plot(faxnomaf1(1:N_2af1),
xmag(1:N_2af1))
xlabel({'Frequency
(Normalised to Nyquist Frequency. ' ...
'1=Nyquist frequency)'})
ylabel('Magnitude');
title('Single-sided
Magnitude spectrum for after Grand Mal seizures (Normalised to Nyquist)');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Single-sided
power spectrum in decibels and Hertz part C
NFFT
= 2^nextpow2(N);
Y
=fft(y1,NFFT)/N;
ft
= fs/2*linspace (0, 1,NFFT/2+1);
figure;
%Power
Spectral Density Estimates Using FFT
%plot
(ft,20*log10(Y(1:NFFT/2+1)));
plot
(ft,2*abs(Y(1:NFFT/2+1)));
xlabel('Frequency
(Hz)')
ylabel('Power
(dB)');
title('Single-sided
Power spectrum (Hertz)for orignal data');
axis
tight
NFFT1
= 2^nextpow2(N1);
Y1
=fft(y1bef,NFFT1)/N1;
ft1
= fs/2*linspace (0, 1,NFFT1/2+1);
figure;
%Power
Spectral Density Estimates Using FFT
%plot
(ft,20*log10(Y(1:NFFT/2+1)));
plot
(ft1,2*abs(Y1(1:NFFT1/2+1)));
xlabel('Frequency
(Hz)')
ylabel('Power
(dB)');
title('Single-sided
Power spectrum (Hertz)for before Grand Mal seizures');
axis
tight
NFFT2
= 2^nextpow2(N2);
Y2
=fft(y1during,NFFT2)/N2;
ft2
= fs/2*linspace (0, 1,NFFT2/2+1);
figure;
%Power
Spectral Density Estimates Using FFT
%plot
(ft,20*log10(Y(1:NFFT/2+1)));
plot
(ft2,2*abs(Y2(1:NFFT2/2+1)));
xlabel('Frequency
(Hz)')
ylabel('Power
(dB)');
title('Single-sided
Power spectrum (Hertz)for during Grand Mal seizures');
axis
tight
NFFT3
= 2^nextpow2(N3);
Y3
=fft(y1after,NFFT3)/N3;
ft3
= fs/2*linspace (0, 1,NFFT3/2+1);
figure;
%Power
Spectral Density Estimates Using FFT
%plot
(ft,20*log10(Y(1:NFFT/2+1)));
plot
(ft3,2*abs(Y3(1:NFFT3/2+1)));
xlabel('Frequency
(Hz)')
ylabel('Power
(dB)');
title('Single-sided
Power spectrum (Hertz)for after Grand Mal seizures');
axis
tight
X_mags=
abs(fft(y1));
bin_vals
= [0 : N-1];
fax_Hz
= bin_vals*fs/N;
N_2
= ceil(N/2);
%plot(fax_Hz(1:N_2),
20*log10(X_mags(1:N_2)))
figure;
plot(fax_Hz(1:2500),
20*log10(X_mags(1:2500)))
xlabel('Frequency
(Hz)')
ylabel('Power
(dB)');
title('Single-sided
Power spectrum (Hertz)');
axis
tight
%Single-sided
power spectrum in dB and frequency on a log scale
X_mag
= abs(fft(y1));
figure;
%subplot(6,1,1);
semilogx(fax_Hz(1:N_2),
20*log10(X_mags(1:N_2)))
xlabel('Frequency
(Hz)');
ylabel('Power
(dB)');
title({'Single-sided
Power spectrum' ...
' (Frequency in shown on a log scale)'});
axis
tight
%%
before
%Single-sided
power spectrum in dB and frequency on a log scale
figure;
X_magsbef=
abs(fft(y1bef));
bin_vals1
= [0 : N1-1];
fax_Hz1
= bin_vals1*fs/N1;
N_2bef
= ceil(N1/2);
semilogx(fax_Hz1(1:N_2bef),
20*log10(X_magsbef(1:N_2bef)));
xlabel('Frequency
(Hz)');
ylabel('Power
(dB)');
title({'Single-sided
Power spectrum for before' ...
' (Frequency in shown on a log scale)'});
axis
tight
%%
during
%Single-sided
power spectrum in dB and frequency on a log scale
figure;
X_magsduring=
abs(fft(y1during));
bin_vals2=
[0 : N2-1];
fax_Hz2
= bin_vals2*fs/N2;
N_2during
= ceil(N2/2);
semilogx(fax_Hz2(1:N_2during),
20*log10(X_magsduring(1:N_2during)));
xlabel('Frequency
(Hz)');
ylabel('Power
(dB)');
title({'Single-sided
Power spectrum for during' ...
' (Frequency in shown on a log scale)'});
axis
tight
figure;
X_magsafter=
abs(fft(y1after));
bin_vals3=
[0 : N3-1];
fax_Hz3
= bin_vals3*fs/N3;
N_2after
= ceil(N3/2);
semilogx(fax_Hz3(1:N_2after),
20*log10(X_magsafter(1:N_2after)));
xlabel('Frequency
(Hz)');
ylabel('Power
(dB)');
title({'Single-sided
Power spectrum for after' ...
' (Frequency in shown on a log scale)'});
axis
tight
N
= length(y1);
xdft
= fft(y1);
xdft
= xdft(1:N/2+1);
psdx
= (1/(fs*N)) * abs(xdft).^2;
psdx(2:end-1)
= 2*psdx(2:end-1);
freq
= 0:fs/length(y1):fs/2;
plot(freq,10*log10(psdx))
grid
on
title('
Power spectrum FFT')
xlabel('Frequency
(Hz)')
ylabel('Power/Frequency
(dB/Hz)')
%The
quadratic mean of the second column is:
%%%%
d
= diff (y1);
g
= diff (d);
so
= rms(y1);
s1
= rms(d);
s2
= rms (g);
%%%%%%%%%%%%%%%%%
signal_complexity
= sqrt((s2^2/s1^2) - (s1^2/so^2));
signal_mobility
= s1/so;
%The
quadratic mean of the second column is:
%%%%
dbefore
= diff (y1bef);
gbefore
= diff (dbefore);
sobefore
= rms(y1bef);
s1before
= rms(dbefore);
s2before
= rms (gbefore);
%%%%%%%%%%%%%%%%%
signal_complexity_before
= sqrt((s2before^2/s1before^2) - (s1before^2/sobefore^2));
signal_mobility_before
= s1before/sobefore;
%The
quadratic mean of the second column is:
%%%%
dduring
= diff (y1during);
gduring
= diff (dduring);
soduring
= rms(y1during);
s1during
= rms(dduring);
s2during
= rms (gduring);
%%%%%%%%%%%%%%%%%
signal_complexity_during
= sqrt((s2during^2/s1during^2) - (s1during^2/soduring^2));
signal_mobility_during
= s1during/soduring;
%The
quadratic mean of the second column is:
%%%%
dafter
= diff (y1after);
gafter
= diff (dafter);
soafter
= rms(y1after);
s1after
= rms(dafter);
s2after
= rms (gafter);
%%%%%%%%%%%%%%%%%
signal_complexity_after
= sqrt((s2after^2/s1after^2) - (s1after^2/soafter^2));
signal_mobility_after
= s1after/soafter;
%%
fractal dimension
%%
fractal dimension
%%
Applied the COMPLETE HIGUCHI FRACTAL DIMENSION ALGORITHM from this website
%http://www.mathworks.com/matlabcentral/fileexchange/30119-complete-higuchi-fractal-dimension-algorithm/content/hfd.m
%
but I modify the kmax, k and m value. I also modified code to load a
%
corrected data.
kmax=9000;
y1new=y1(:)';
Lmk=zeros(kmax,kmax);
for
k=1:kmax,
for m=1:k,
Lmki=0;
for i=1:fix((N-m)/k),
Lmki=Lmki+abs(y1new(m+i*k)-y1new(m+(i-1)*k));
end;
Ng=(N-1)/(fix((N-m)/k)*k);
Lmk(m,k)=(Lmki*Ng)/k;%Here is the
problem in your code, Mr. Tikkuhirvi & Mr. Aino
end;
end;
Lk=zeros(1,kmax);
for
k=1:kmax,
Lk(1,k)=sum(Lmk(1:k,k))/k;
end;
lnLk=log(Lk);
lnk=log(1./[1:kmax]);
b=polyfit(lnk,lnLk,1);
xhfd=b(1);
varargout={xhfd,lnk,lnLk,Lk,Lmk};
figure;
subplot
(4,1,2);
plot (lnk, lnLk);
title
('plot of the ln(l(k)) versus ln (l/k) of EEG signal of a grand mal assault ');
xlabel
('ln (l/k)');
ylabel
('ln(l(k))');
grid
on;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
before
kmaxbef=500;
y1newbef=y1bef(:)';
Lmkbef=zeros(kmaxbef,kmaxbef);
for
kbef=1:kmaxbef,
for mbef=1:kbef,
Lmkibef=0;
for i=1:fix((N1-mbef)/kbef),
Lmkibef=Lmkibef+abs(y1newbef(mbef+i*kbef)-y1newbef(mbef+(i-1)*kbef));
end;
Ngbef=(N1-1)/(fix((N1-mbef)/kbef)*kbef);
Lmkbef(mbef,kbef)=(Lmkibef*Ngbef)/kbef;%Here is the problem in your
code, Mr. Tikkuhirvi & Mr. Aino
end;
end;
Lkbef=zeros(1,kmaxbef);
for
kbef=1:kmaxbef,
Lkbef(1,kbef)=sum(Lmkbef(1:kbef,kbef))/kbef;
end;
lnLkbef=log(Lkbef);
lnkbef=log(1./[1:kmaxbef]);
b1=polyfit(lnkbef,lnLkbef,1);
xhfdbefore=b1(1);
varargoutbef={xhfdbefore,lnkbef,lnLkbef,Lkbef,Lmkbef};
%figure;
subplot
(4,1,2);
plot (lnkbef, lnLkbef);
title
('plot of the ln(l(k)) versus ln (l/k) of EEG signal before grand mal seizures
');
xlabel
('ln (l/k)');
ylabel
('ln(l(k))');
grid
on;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
during
kmaxduring=500;
y1newduring=y1during(:)';
Lmkduring=zeros(kmaxduring,kmaxduring);
for
kduring=1:kmaxduring,
for mduring=1:kduring,
Lmkiduring=0;
for i=1:fix((N2-mduring)/kduring),
Lmkiduring=Lmkiduring+abs(y1newduring(mduring+i*kduring)-y1newduring(mduring+(i-1)*kduring));
end;
Ngduring=(N2-1)/(fix((N2-mduring)/kduring)*kduring);
Lmkduring(mduring,kduring)=(Lmkiduring*Ngduring)/kduring;%Here is the
problem in your code, Mr. Tikkuhirvi & Mr. Aino
end;
end;
Lkduring=zeros(1,kmaxduring);
for
kduring=1:kmaxduring,
Lkduring(1,kduring)=sum(Lmkduring(1:kduring,kduring))/kduring;
end;
lnLkduring=log(Lkduring);
lnkduring=log(1./[1:kmaxduring]);
b2=polyfit(lnkduring,lnLkduring,1);
xhfdduring=b2(1);
varargoutduring={xhfdduring,lnkduring,lnLkduring,Lkduring,Lmkduring};
%figure;
subplot
(4,1,3);
plot (lnkduring, lnLkduring);
title
('plot of the ln(l(k)) versus ln (l/k) of EEG signal during grand mal seizures
')
xlabel
('ln (l/k)');
ylabel
('ln(l(k))');
grid
on;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
after y1after
kmaxaf=500;
y1af=y1after(:)';
Lmkaf=zeros(kmaxaf,kmaxaf);
for
kaf=1:kmaxaf,
for maf=1:kaf,
Lmkiaf=0;
for i=1:fix((N3-maf)/kaf),
Lmkiaf=Lmkiaf+abs(y1af(maf+i*kaf)-y1af(maf+(i-1)*kaf));
end;
Ngaf=(N3-1)/(fix((N3-maf)/kaf)*kaf);
Lmkaf(maf,kaf)=(Lmkiaf*Ngaf)/kaf;%Here
is the problem in your code, Mr. Tikkuhirvi & Mr. Aino
end;
end;
Lkaf=zeros(1,kmaxaf);
for
kaf=1:kmaxaf,
Lkaf(1,kaf)=sum(Lmkaf(1:kaf,kaf))/kaf;
end;
lnLkaf=log(Lkaf);
lnkaf=log(1./[1:kmaxaf]);
b3=polyfit(lnkaf,lnLkaf,1);
xhfdaf=b3(1);
varargoutaf={xhfdaf,lnkaf,lnLkaf,Lkaf,Lmkaf};
%figure;
subplot
(4,1,4);
plot (lnkaf, lnLkaf);
title
('plot of the ln(l(k)) versus ln (l/k) of EEG signal after grand mal seizures
');
xlabel
('ln (l/k)');
ylabel
('ln(l(k))');
grid
on;
close
all;
clear
all;
clc;
%%
problem 6.5
%%import
the data in the file "P-6_5.xls" and plot the signal
%load('p_6_5.mat')
%problem6_3
=xlsread('\\tsclient\E\BIOM480A3\HW4\test.xls');
problem6_3
=xlsread('I:\BIOM480A3\HW4\test.xls');
y1
= problem6_3(:);
%fs
= 173.61;% sampling rate or frequency (Hz)
N
= length(y1);% find the length of the data per second
%T
= 1/fs % period between each sample
ls
= size(y1); %% size
fs2
= 1/ N;% find the sampling rate or frequency
t
= (0 : N-1) /fs2;
t1
= (0 : N-1)'/N;%t = (0:1:length(y1)-1)/fs; % define time
Nyquist
= fs2/2;
figure;
plot(y1,'b');
title
('plot of the EEG signal caputure under fixed condition')
xlabel
('time (sec)');
ylabel
('Amplitute (v)');grid on;
%compute
the mean of x
means
= mean(y1);
variance
= var(y1);
xbar
= y1(1);
for
i=2:N
xbar
= xbar + y1(i);
end
xbar
= xbar/N;
%compute
the variance of x
var
= std(y1)^2;
conv=
cov(y1);
corr
= corrcoef(y1);
%%
part C
%f1
= normpdf(y1);
f1
= normpdf(y1);
figure;
plot
(f1);
title
('plot of the PDF of the stochastic process')
xlabel
('time (sec)')
ylabel
('Amplitute (v)')
grid
on;
figure;
subplot(2,1,1);
hist(y1);
title('Histogram of Raw Data');
[f,xi]
= ksdensity(y1); %pdf estimate
subplot(2,1,2);
plot(xi,f);
title('plot of the PDF of the stochastic process');
xlabel
('random process numbers');
ylabel
('Gaussian distribution');
figure;
subplot(2,2,1);
hist
(f1);
title({'plot
of the PDF of the PDF stochastic process..'
'without means and variance'});
xlabel
('random process numbers');
ylabel
('Gaussian distribution');
%figure;
subplot(2,2,2);
f12=
normpdf(y1, means, variance);
hist(f12);
title({'plot
of the PDF of the PDF stochastic process...'
'with means and variance'});
xlabel
('random process numbers');
ylabel
('Gaussian distribution');
%%
part d
xcor
= xcorr (y1);
figure;
plot(xcor);
title
('plot of the autocorrelation of the EEG')
xlabel
('time (sec)')
ylabel
('Amplitute (uv)')
%part
e
Corrlation_partC
= corrcoef(f);
Corrlation_partC1
= corrcoef(xi,f);
Corrlation_partC2
= corrcoef(y1);
xdft
= fft(y1);
xdft
= xdft(1:N/2+1);
psdx
= (1/(fs2*N)) * abs(xdft).^2;
psdx(2:end-1)
= 2*psdx(2:end-1);
freq
= 0:fs2/length(y1):fs2/2;
figure;
plot(freq,10*log10(psdx))
grid
on
title('the
power spectrum of the process using 10*log10(fft(signal)) ')
xlabel('Frequency
(Hz)')
ylabel('Power/Frequency
(dB/Hz)')
psdx1
= (1/(length(y1))) * abs(xdft).^2;
figure;
plot
(psdx1)
title('the
power spectrum of the process using FFT')
xlabel('Frequency
(Hz)')
ylabel('Power/Frequency
(dB/Hz)')
From where EEG signal is taken?
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