brain computer interface

i need to do matlab codes to classify an eeg signal into alpha, beta, gamma, theta, and delta according to their frequencies.I have the data.And convert to excel file.I could it and can see the data.But I have to find the frequency.Could anyone guide me.

[Jan]

Is the conversion to an Excel file really helpful? This will slow down the data import remarkably.

What kind of answer do you expect? A source, where you can find the definitions of the different wave types? The name of a commercial software, which can solve this? A complete M-file, which solves the problem, although the most details are not known yet?

I suggest to post the existing code and ask specific questions. Your former question have not been answered yet. Therefore I suggest this thread to: http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer

Answer 1

[Kosai]

prabakaran, if you have the wavelet toolbox, i think this code will help you to extract the all-bands(alpha,beta,....), i will assume that the Fs of your egg raw is 1000Hz

                S = "your EEG-Data-Raw";
                waveletFunction = 'db8';
                [C,L] = wavedec(S,8,waveletFunction);
                %%Calculation The Coificients Vectors
                cD1 = detcoef(C,L,1);                   %NOISY
                cD2 = detcoef(C,L,2);                   %NOISY
                cD3 = detcoef(C,L,3);                   %NOISY
                cD4 = detcoef(C,L,4);                   %NOISY
                cD5 = detcoef(C,L,5);                   %GAMA
                cD6 = detcoef(C,L,6);                   %BETA
                cD7 = detcoef(C,L,7);                   %ALPHA
                cD8 = detcoef(C,L,8);                   %THETA
                cA8 = appcoef(C,L,waveletFunction,8);   %DELTA

                %%%%Calculation the Details Vectors
                D1 = wrcoef('d',C,L,waveletFunction,1); %NOISY
                D2 = wrcoef('d',C,L,waveletFunction,2); %NOISY
                D3 = wrcoef('d',C,L,waveletFunction,3); %NOISY
                D4 = wrcoef('d',C,L,waveletFunction,4); %NOISY
                Gamma = wrcoef('d',C,L,waveletFunction,5); %GAMMA
                Beta  = wrcoef('d',C,L,waveletFunction,6); %BETA
                Alpha = wrcoef('d',C,L,waveletFunction,7); %ALPHA
                Theta = wrcoef('d',C,L,waveletFunction,8); %THETA
                Delta = wrcoef('a',C,L,waveletFunction,8); %DELTA
...

[Nikitha Joshi]

Can you please send me the matlab code for brain controlled robotic arm

[krishna chaithanya Joru]

thanks

Answer 2

[Aaditya Kalsi]

In general, you may be able to solve such problems by Classification. You may extract some features from your dataset, in your case, consider:

First collect ideal sample FFTs for each type (ALPHA BETA etc.)

1. FFT of length N (you may choose N) as your feature space. 2. Use CORRCOEFF on the ABS of the current times FFT with the ideal FFTs. The one with max. value is the type.

This would be an easy starting point without relying on specific toolboxes.

Answer 3

[DSP]

Hello Kalsi, I have the same problem. Can you please provide some MATLAB code to extract EEG features like correlation coefficient etc.. in details.Thanks a lot.