搜索资源列表
artifact
- Artifact removal of EEG signal EEG signal
EEG_Enhancer
- A EEG Enhancer with kalman filter enhancer
PCATEST
- pca分类程序，主要用于脑电信号的分类。具有较好的分类精度！-pca classification procedures, mainly for the classification of EEG signals. Has better classification accuracy!
PhaSpaRecon
- 应用多变量相空间重构对分析脑电信号Multivariate analysis of the phase space reconstruction of the EEG-Multivariate analysis of the phase space reconstruction of the EEGMultivariate analysis of the phase space reconstruction of the EEG
MD5
- SSVEP脑电分析算法，计算脑电的频率和相位信息，并做出判断-SSVEP EEG analysis algorithm to calculate EEG frequency and phase information, and make judgments
100
- edf reading for eeg using for biomedical engineer
ss
- REMOVAL OF NOISE FROM ECG (ELECTROCARDIOGRAPHY) BY USING MATLAB. EEG (Electroencephalograph) recording from the scalp has biological artifacts and external artifacts. Biological artifacts, which are generated, can be EMG (Electromyography) sign
ERCoh2
- 对脑电信号进行滤波、快速傅里叶变换（FFT）及自相关、互相关计算，以观察每一通道脑电的相关性-On EEG filtering, fast Fourier transform (FFT) and autocorrelation, cross correlation calculation, in order to observe the correlation of each channel EEG
new_guerrero_embc2009
- EEG FEATURE EXTRACTION
Biomed-Reader
- These functions have been developed to watch and analyze EEG/Video/Accelero(3A)/Magneto(3M) data in the context of the Epimouv/Capametrim project. The project aim is to recognized epileptic movement disorders by determining regions of interests on
load
- code to decode raw eeg data..eeg sample matrices
code2
- simulation of raw eeg signal.
Bayesian_Wavelet_Network
- EEG signal classification using wavelet feature extraction
whmt1d
- EEG signal classification using wavelet feature extraction
deltaforthnet131
- EEG signal classification using wavelet feature extraction
vks
- EEG signal classification using wavelet feature extraction
04154991
- EEG-Based Lapse Detection With High Temporal resolution
wavelet
- 小波变换用于脑电信号处理，可以很好的对脑电信号进行时频分析-Wavelet transform for EEG signal processing, can be very good for time-frequency analysis of EEG
mu-fisher
- 基于mu节律能量的140组脑电信号的决策分类，从3s到9s之间不同决策时间点的识别率，最高为85 -140 mu rhythm-based energy group decision-making EEG classification, from 3s to 9s different decision points in time between the recognition rate up to 85
Untitled
- EEG信号多分辨率分析,可以用改文件对EEG信号进行小波处理，即过滤去噪等。-EEG signals analysis