Description: 基于小波包分解的脑电信号特征提取.格式是caj后缀的，大家看的时候可能得有CAJViewer 这个软件-Based on wavelet packet decomposition of the EEG feature extraction. CAJ suffix format is, we see there may be a time when the software CAJViewer Platform: |
Size: 240640 |
Author:lynn |
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Description: 脑电想象运动的csp特征提取分类算法 matlab平台，通过投票可以直接扩展到多类-Imagine the movement csp EEG feature extraction classification algorithm matlab platform, through the vote can be directly extended to multiple classes Platform: |
Size: 33792 |
Author:段放 |
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Description: EEG处理过程中用到的一些程序，如预处理，特征提取等-EEG processing some of the procedures used, such as preprocessing, feature extraction Platform: |
Size: 2738176 |
Author:sytangqin |
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Description: ：基于脑电信号的身份识别是通过采集试验者的脑部信号来进行身份认证。对于同一个外部刺激或者主体在思考同一个
事件的时候，不同人的大脑所产生的认知脑电信号不同。选取与运动意识想象有关的电极后，分析不同个体在特定状况下脑
电的个体差异，采用以回归系数、能量谱密度、相同步、线性复杂度多种信号处理结合方法对运动想象脑电信号进行处理来
进行特征提取。组合多元特征向量并运用多层BP 神经网络对不同个体的脑电信号进行分类，并在不同的意识想象及不同数
据长度、不同的波段对试验者进行识别率验证分析。结果表明，不同运动想象的平均识别率均在80 以上，其中以想象舌头
运动的识别率较高，达到90.6 ，不同波段的识别率也表明意识想象的模式及相应波段对身份认别有较大的影响。-EEG-based identification to authenticate through the acquisition of experimental brain signals. For the same external stimuli, or the main thinking of the same
Event, different people s brains produced by cognitive EEG. Select imagine the electrodes and movement awareness, analysis of different individuals in a particular situation brain
Individual differences in electricity, the use of regression coefficients, the energy spectral density, phase synchronization, the linear complexity of a variety of signal processing combined with motor imagery EEG
For feature extraction. The combination of multiple feature vectors and the use of multi-layer BP neural network to classify the EEG signals of different individuals, and in a different sense of imagination and a different number of
Length, the band on the test to verify the analysis of the recognition rate. The results show that the average recognition rates of different motor imagery in more than 80 , which to imagine the tongue
The m Platform: |
Size: 551936 |
Author:王闯杰 |
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Description: 最新版的biosig4octmat程序，适用于EEG信号的特征提取和分类，以及分类器的性能评价-The the the latest version biosig4octmat program applies to EEG signal feature extraction and classification, and the performance evaluation of the classifier Platform: |
Size: 7012352 |
Author:Bill |
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Description: 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 Platform: |
Size: 1024 |
Author:王冬 |
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Description: 脑电信号的硬阈值和软阈值去噪算法，是对脑电信号消噪的基本消噪能够成功取得脑电消噪的目的，为脑电信号的特征提取做好充分准备-EEG hard threshold and soft thresholding algorithm is the basic noise cancellation EEG de-noising can successfully achieve the purpose of de-noising EEG, EEG feature extraction is fully prepared Platform: |
Size: 4096 |
Author:dingtong |
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Description: CLASSIFICATION OF DATA RELATED EEG BASED SIGNAL ANALYSIS AND FEATURE EXTRACTION TECCHNIQUE Platform: |
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Author:humamaheen |
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Description: CLASSIFICATION OF DATA RELATED EEG BASED SIGNAL ANALYSIS AND FEATURE EXTRACTION TECCHNIQUE Platform: |
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Author:humamaheen |
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Description: 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. Platform: |
Size: 1024 |
Author:李贤 |
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Description: 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 (electrodes), in multiple trials,
epochs, for multiple subjects and with different tasks, conditions. Moreover, during processing and
analysis, dimensionality of the data could be augmented due to expression of the data into sparse
domain (time-frequency representation) by different transforms such as STFT, wavelets. That means
data itself is naturally a tensor, and has multilinear structures. Standard approaches which analyze
such data by considering them as vectors or matrices might be not suitable due to risk of losing the
covariance information among various modes. To discover hidden multilinear structures, features
within the data, the analysis tools should reflect the multi-dimensional structure of the data Platform: |
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Author:李新会 |
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Description: 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) Platform: |
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Author:Lonathe |
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Description: 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 experiment is CSP spatial filtering, and FLDA is used for feature classification. Finally, good results are obtained) Platform: |
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Author:chewfy |
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