EEG Signal Extraction Using FFT Spectral Analysis

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Resource Overview

FFT spectral analysis can be employed to extract EEG signals from various frequency bands. The extracted signals enable diagnosis of neurological disorders and analysis of brain electrical activity and functional states. Implementation involves: 1) Converting experimental EEG data files (pre-processed with 50Hz notch filtering) to text format (e.g., 0661.txt) for MATLAB compatibility; 2) Importing data into MATLAB, extracting Fp1 channel signals, applying FFT to isolate α, β, θ, and δ bands, and performing inverse FFT for time-domain reconstruction; 3) Calculating power spectral density for each frequency band.

Detailed Documentation

This document describes EEG signal extraction using FFT spectral analysis to enable comprehensive signal processing. The extracted multiband EEG signals facilitate neurological disease diagnosis and analysis of cerebral electrical activity and functional states. The technical implementation involves the following steps:

1. First, convert experimentally acquired EEG data files (pre-processed with 50Hz notch filtering) to text format, generating MATLAB-compatible EEG signal data (e.g., 0661.txt). This conversion typically uses file I/O operations like fread/fwrite functions for format standardization.

2. Subsequently, import the data into MATLAB environment and extract Fp1 channel EEG signals using indexing operations. Apply FFT transformation (fft function) to isolate α (8-13 Hz), β (13-30 Hz), θ (4-8 Hz), and δ (0.5-4 Hz) frequency bands through frequency-domain filtering. Perform inverse FFT (ifft function) to reconstruct time-domain signals for each band, preserving phase information.

3. Finally, compute power spectral density for each band using pwelch function or squared magnitude of FFT coefficients (abs(fft_result).^2), enabling quantitative analysis of signal energy distribution across frequency bands.

These steps facilitate detailed EEG signal characterization and provide enhanced insights into brain activity patterns through spectral decomposition techniques.

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