搜索资源列表
New-Combining-Approach-for-EEG-Source-Localizatio
- A Lecture delivered by Munsif Ali Jatoi on a Research paper at UTP, Malaysia.
dependentcomponent
- 本文简要地回顾了ICA的发展历史和主要算法，综述了它在脑电信号处理中的应用及研究进展，并指出了需要进一步研究解决的问题。-This article briefly reviews the history of the development of the ICA algorithm EEG signal processing applications and research progress, and pointed out the need for further research to s
EEG-Complexity-as-a-Measure-of-Depth-of-anesthesi
- algorithm for signal processing
Epileptic-Seizure-Detection
- 利用小波变换和样本熵对癫痫脑电进行识别的文章-Recognition article epileptic EEG using wavelet transform and sample entropy
ar-model
- 几篇有关利用ar模型对癫痫脑电信号进行识别的英文文章，比较权威，是参考的好资料-Few articles about the use of ar model of epileptic EEG recognition of English articles, more authoritative reference information
EEG-SEMG
- 表面肌电信号与肢体运动直接相关，肢体的不同动作具有不同的肌肉收缩模式，这些模式的差别反映在表面肌电信号特征的差异上-Surface EMG and limb movements directly related to the different movements of the limbs have different muscle contraction mode, the difference between these patterns reflect differences in sEM
SVM_JNE2010
- SVM EEG signal classification
gasvm
- 利用遗传算法对SVMk进行优化,并将其用于脑电信号的分类中-By using the genetic algorithm to optimize SVMk, and use it to the classification of the eeg signals
xinhaofenlei
- 脑电信号的特征提取与分类，大家可以查阅，很有帮助的-EEG feature extraction and classification, we can access helpful
xiaobo-svm
- 关于脑机接口的文献，基于SVM和小波分析的脑电信号分类方法-Literature on brain-computer interface the EEG classification method based on SVM and Wavelet Analysis
fuxiaobo
- 脑机接口文献 复小波在脑电同步性研究中的应用 有用-Of complex wavelet of brain-computer interface literature in EEG synchronization study
wenxiancankao
- 基于脑电信号的聚类分析的文献读后感并详细地总结了一下，希望对你们有用，个人观点-EEG-based clustering analysis of literature book review and detailed summary of what you want to be useful, personal point of view
juleifenxidenaodianfenxi
- 基于聚类分析的脑电信号数据处理，详细讲了脑电的基础知识然后讲了具体的脑电采集，然后是分类-EEG data processing based on cluster analysis, saying more about the basics of the EEG and then talk about specific EEG acquisition and classification
ApEn_v1
- this is very good and important for work with eeg data.
data
- demo code source for eeg recognition
higher-order-staistics
- Classifying mental tasks based on features of highe order statistics from EEG signals in bci
2
- Feature Extraction from EEG
KHOBB
- Feature Extraction from EEg
NCSCIT01_086_1815459
- Feature Extraction from eeg
109_stastny
- Feature Extraction from EEG