Explore MATLAB source code curated for "脑电信号" with clean implementations, documentation, and examples.
This program provides source code for building neural networks with wavelet functions, designed for analyzing various biomedical signals such as ECG and EEG signals through wavelet-based feature extraction and neural network classification.
A MATLAB program for calculating approximate entropy, designed specifically for EEG signal analysis. This implementation provides a computational framework to quantify signal complexity and extract meaningful patterns from brainwave data. The code serves as a reference implementation and includes parameter configurations for window size and delay time optimization. Users are encouraged to provide feedback for potential improvements.
Implementation of sliding detrended fluctuation analysis featuring comprehensive internal documentation. The MDFA configuration file allows adjustable parameters for optimized results. The variable 'x' contains EEG signal data (not included in upload). This modular algorithm can be adapted for various data types through parameter customization and includes configurable window sizing and detrending methodologies.
Implementation of Fisher Linear Classifier using MATLAB for EEG signal classification with BCI competition datasets, featuring feature extraction and signal pattern recognition
Experimental results using S-function demonstrate that ICA can effectively isolate multiple artifact signals including ECG and EOG contained within EEG signals, with practical implementation insights for signal processing algorithms.
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.
Common Spatial Pattern (CSP) filtering is a method for processing EEG signals that maximizes the difference in covariance between two classes of motor imagery EEG data, facilitating subsequent classification tasks.
Linear Discriminant Analysis classifier algorithm for brain signal discrimination with feature extraction and classification techniques
This program provides source code for building neural networks based on wavelet functions, designed for analyzing ECG signals, EEG signals, and various biological signals with enhanced feature extraction capabilities.
Four-Level Wavelet Decomposition for EEG Signals - This program implements a four-layer wavelet decomposition algorithm for EEG signal processing, accompanied by comprehensive MATLAB programming course materials designed to facilitate understanding of wavelet-based signal analysis techniques.