搜索资源列表
Feature_Extraction
- 筛选的几篇脑电节律提取的文章，可以用于其它信号处理~ 【基于改进小波变换的EEG分析】【基于小波变换的动态脑电节律提取】【脑电信号的特征节律信号提取】【a、β、δ、θ和40Hz波实时检测器】-Screening of several EEG extracted article, can be used for other signal processing ~ 【EEG based on improved wavelet analysis】 【based on wavelet transf
EEG-power-spectrum-estimation-
- 本科毕业时做的男女左右运动时提取脑电信号的功率谱，是分频提取的，也就是左、右手运动是的不同特征，程序简单易懂，还附有脑电信号、程序说明-Graduate men and women to do about movement of EEG power spectrum is divided extraction, that is, the left and right movement is different features, the program is simple to understa
wxj_CSP
- 脑电特征提取的典型特征提取算法——共同空间模式方法，本人已亲自调试，并用于论文的写作，是一个提取脑电特征的利器-Eeg feature extraction of typical feature extraction algorithm, joint space model method, I have personally debugging, and used for thesis writing, is a feature extraction of eeg
wave1
- 小波变换的特征提取，用于情感识别方法，基于脑电信号-Wavelet feature extraction method for emotion recognition based on EEG
Renyi
- 计算一维时间序列的Renyi熵，可作为脑电信号的特征提取方法，从而对脑电的复杂度进行分析-The Renyi entropy of one dimensional time series can be calculated as a feature extraction method of EEG signal, which can be used to analyze the complexity of EEG.
Entropy_Approximate
- 计算时域信号的近似熵程序,用于脑电信分析中的特征提取，计算脑电信号的近似熵-Calculate the time domain signal approximate entropy program features used in telecommunications analysis of brain extracts, calculated EEG approximate entropy
NFEA
- 张量分解提取生物学特征，NFEA: Tensor Toolbox for Feature Extraction and Applications- Data in modern applications such as BCI based on EEG signals often contain multi-modes due to mechanism of data recording, e.g. signals recorded by multiple-sensors (elec
eeg_feature_extraction
- 对eeg的五个波段进行提取, 脑电信号数据原始采集的脑电，输出5个不同波段幅值，(extract 5 wave bands of eeg signals)
CSP_LDA
- csp特征提取，lda进行分类，四分类脑电信号处理，亲测可用(CSP feature extraction, LDA classification, four classification of EEG signal processing, pro test available)
MATLAB
- 采用AR模型对脑电信号进行特征提取，亲测可用！！(Using AR model to extract EEG features, pro test can be used!!)
BCI_MI_CSP_DNN
- BCI_MI_CSP_DNN是一种基于matlab的运动图像脑电信号分类程序。 基于matlab深度学习工具箱编写了BCI_MI_CSP_DNN程序 本程序的原理基于CSP和DNN算法 这个程序的性能是基于BCI竞赛II数据集II 提出了一种基于深度学习的运动图像脑电信号分类方法。在预处理原始脑电图信号的基础上，采用共空间模型(CSP)方法提取脑电图特征矩阵，并将其输入深度神经网络(DNN)进行训练和分类。我们的工作在BCI Competition II Dataset III上进行了实
cntData_CSP_FLDA
- 本算法针对运动想象的脑电数据，进行预处理并后续用分类器做分类。 该实验所用的的脑电特征提取方法主要是csp空间滤波，并后续用FLDA来进行特征分类。最终得到较好的效果(In this algorithm, the EEG data of motion imagination are preprocessed and then classified by classifier. The main feature extraction method of EEG in this experime