EEG Signal Processing Using FFT (Part 3)

FFT spectrum analysis enables effective extraction of EEG signals, enhancing our understanding of brain electrical activity. By isolating specific frequency bands (α, β, θ, δ), we can perform detailed diagnosis of neurological conditions and analyze brain tissue electrophysiology and functional states. The implementation workflow involves:

1. Converting experimental EEG data files to text format after 50Hz notch filtering, resulting in Matlab-compatible data file 0661.txt. This preprocessing step ensures clean data input using Matlab's file reading functions like readtable() or load().

2. Importing data into Matlab environment and extracting Fp1 channel EEG signals. The implementation uses Fast Fourier Transform (fft() function) for frequency-domain decomposition, followed by bandpass filtering to isolate target bands. Inverse FFT (ifft()) reconstructs time-domain signals for each band, maintaining phase information through proper handling of complex coefficients.

3. Calculating power spectral density for each frequency band using periodogram methods (pwelch() function) or direct squared magnitude of FFT coefficients. This quantifies energy distribution across frequencies, crucial for analyzing brain state variations and pathological patterns.

This comprehensive approach facilitates multidimensional EEG analysis, providing valuable insights into brain activity dynamics and functional characteristics.