FFT频谱 Resources

Showing items tagged with "FFT频谱"

FFT spectral analysis enables extraction of EEG signals from different frequency bands. These extracted signals can be used for diagnosing brain disorders and analyzing electrical activity patterns in brain tissue and functional states. The workflow includes: 1. Converting experimental EEG data files to text format (after 50Hz notch filtering) to obtain Matlab-compatible data (0661.txt). 2. Importing data into Matlab, extracting Fp1 channel signals, applying FFT to isolate α, β, θ, and δ bands, then performing inverse FFT for time-domain reconstruction. 3. Calculating power spectra for each frequency band.

MATLAB 243 views Tagged
Download

This program plots the FFT spectrum and power spectrum of a simulated signal. It utilizes Empirical Mode Decomposition (EMD) to decompose the signal, visualizes the resulting intrinsic mode functions (IMFs), and computes instantaneous envelopes and frequencies. Additionally, it generates the Hilbert-Huang spectrum and Hilbert marginal spectrum for comprehensive time-frequency analysis.

MATLAB 268 views Tagged
Download

EEG signal extraction can be performed using FFT spectrum analysis. The extracted EEG signals from different frequency bands enable diagnosis of neurological disorders and analysis of brain electrical activity and functional states. Key implementation steps include: 1. Converting experimental EEG data (pre-filtered with 50Hz notch) to text format for Matlab compatibility (0661.txt). 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. Computing power spectral density for each frequency band.

MATLAB 216 views Tagged
Download

FFT spectrum analysis enables effective extraction of EEG signals from different frequency bands. The extracted EEG waveforms (α, β, θ, δ) can be utilized for diagnosing neurological disorders and analyzing brain electrical activity and functional states. The implementation involves: 1. Converting experimental EEG data files to text format with 50Hz notch filtering, resulting in Matlab-compatible data files like 0661.txt. 2. Importing data into Matlab, extracting Fp1 channel EEG signals, performing FFT transformations to isolate frequency bands, and applying inverse FFT for time-domain reconstruction. 3. Calculating power spectrum density for each frequency band to quantify signal characteristics.

MATLAB 218 views Tagged
Download