EEG Signal Processing using FFT Analysis (Part 2)

FFT spectral analysis provides an effective method for extracting EEG signals. The extracted signals from different frequency bands enable diagnosis of brain disorders and detailed analysis of electrical activity patterns in brain tissue and functional states. The implementation workflow involves the following steps:

1. Convert experimental EEG data files to text format (post 50Hz notch filtering) to generate Matlab-compatible EEG signal data (0661.txt). This preprocessing step typically uses functions like fopen and fwrite for file format conversion while ensuring proper sampling rate specification.

2. Import data into Matlab environment using load or textread functions, then extract EEG signals from the Fp1 channel. Apply FFT transformation using fft() function to isolate α (8-13Hz), β (13-30Hz), θ (4-8Hz), and δ (0.5-4Hz) frequency bands. Implement band-pass filtering through frequency domain masking before performing inverse FFT using ifft() function to reconstruct time-domain signals for each band.

3. Calculate power spectra for each frequency band using periodogram or pwelch functions, which involve squaring the magnitude of FFT coefficients and normalizing by frequency resolution to obtain power spectral density estimates.

This comprehensive analysis pipeline yields detailed EEG signal characteristics that significantly contribute to research on brain disorders and functional state assessment.