Explore MATLAB source code curated for "脑电信号" with clean implementations, documentation, and examples.
Preprocessing EEG signals involves signal filtering, downsampling, and noise removal to prepare data for analysis, with code implementation details for digital filtering and noise reduction algorithms.
A time-frequency processing method for extracting EEG signal features, which can refer to HHT (Hilbert-Huang Transform) data, involving empirical mode decomposition and Hilbert spectral analysis for non-stationary signal characterization.
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.
EEG Signal Processing with Fast Independent Component Analysis for removing ECG, EOG, and EMG artifacts, demonstrating effective noise reduction performance
This program utilizes MATLAB's signal analysis functions to perform comprehensive analysis and transformation of EEG signals, providing significant support for signal processing tasks through implementations of spectral analysis, wavelet transforms, and time-frequency decomposition techniques.
This undergraduate thesis project implemented power spectrum extraction of EEG signals during left/right hand movements in male and female participants using frequency-division analysis to isolate distinctive features for left and right hand motions. The MATLAB-based program features clear modular structure with functions for signal preprocessing, frequency band separation, and power spectrum calculation using FFT algorithms. Includes complete EEG datasets and detailed program documentation.
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.
Digital filters effectively process EEG signals by removing unwanted components and achieving noise reduction, typically implemented using FIR or IIR filter designs with frequency-selective techniques.
The author developed an EEG signal interface program using MATLAB, implementing wavelet decomposition for efficient extraction of brainwave rhythms from EEG signals.
Original code implementation featuring EEG signal workspace data provided by my supervisor, demonstrating classification using BP neural network - ideal for beginners learning neural networks with practical examples.