wavelet decomposition and power spectrum of an EEG signal

Hi everyone! I'm poor in Matlab and i need help for a research. I've to compute the power spectrum of brain rhythms (alpha, beta, gamma, theta, delta) of an EEG signal. The signal has the following characteristics:

• data.mat (<19x265984 double>)
• Sampling Frequency --> 128Hz.

I'm not sure how many level need to obtain the band-frequency with the wavelet decomposition, but i suppose that 5 is the right number. I've read many posts on this forum, so i tried to process the following code:

% Frequency of sampling and time's sampling
 Fs=128;
 t=0:1/Fs:1

% Select the channels from the signal
 for k = 1:19,
     v = genvarname('Ch', who);
     eval([v ' =data(k,:)']);
 end;

% Define the wavelet's name and the level for decomposition
 waveletName='db5';
 level=5;

% Multilevel 1-D wavelet decomposition for channel 0
 [c0,l0]=wavedec(Ch,level,waveletName);

% 1-D detail coefficients for ch0
 cD1 = detcoef(c0,l0,1); %NOISY 
 cD2 = detcoef(c0,l0,2); %GAMMA 
 cD3 = detcoef(c0,l0,3); %BETA 
 cD4 = detcoef(c0,l0,4); %ALPHA 
 cD5 = detcoef(c0,l0,5); %THETA 
 cA5 = appcoef(c0,l0,waveletName,5); %DELTA

% Reconstruct single branch from 1-D wavelet coefficients for ch0
 D1 = wrcoef('d',c0,l0,waveletName,1); %NOISY 
 D2 = wrcoef('d',c0,l0,waveletName,2); %GAMMA 
 D3 = wrcoef('d',c0,l0,waveletName,3); %BETA 
 D4 = wrcoef('d',c0,l0,waveletName,4); %ALPHA 
 D5 = wrcoef('d',c0,l0,waveletName,5); %THETA 
 A5 = wrcoef('a',c0,l0,waveletName,5); %DELTA

If everything is correct, what should I do to apply the power spectrum to the obtained rhythms? Thanks!

[Favour Dada]

that is enough to extract the ppower spectrums but you should plot the graphs for beta, theta, gamma, beta in the denooised level.