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fs = 2000; Clean = EEG; Raw = noisy_eeg; tx =[0:length(EEG)-1]/fs; %grid on; d = designfilt('bandstopiir', 'FilterOrder', 6, 'HalfPowerFrequency1', 65, 'HalfPowerFrequency2', 96, 'DesignMethod', 'butter', 'SampleRate',

fs = 2000; Clean = EEG; Raw = noisy_eeg; tx =[0:length(EEG)-1]/fs; %grid on; d = designfilt('bandstopiir', 'FilterOrder', 6, 'HalfPowerFrequency1', 65, 'HalfPowerFrequency2', 96, 'DesignMethod', 'butter', 'SampleRate', fs); fvtool(d,'fs',fs) Filtered_Signal = filtfilt(d, Raw); Error = rmse(Clean,Filtered_Signal) t_similarity = corrcoef(Clean,Filtered_Signal) %f_similarity = mscohere(Clean,Filtered_Signal) signal_quality = snr(Raw,Filtered_Signal) subplot(3,1,1); plot(tx,Filtered_Signal) ylabel('Voltage (V)') xlabel('Time (s)') title('filtered signal') legend('Unfiltered','Filtered') grid subplot(3,1,2); plot(tx, Clean); xlabel('Time [s]'); ylabel('Amplitude'); title('Original signal'); subplot(3,1,3); grid on; plot(tx, Raw); xlabel('Time [s]'); ylabel('Amplitude'); title('Raw Signal');

[EXPLAIN DETAILS STEP BY STEP EACH LINE ]AND SHOW THE FUNCTION OF EEG ,IIR[MATLAB]

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