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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
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Size: 33792 |
Author: 段放 |
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Description: 在目前以运动想象为基础的脑机接口(Brain-Computer Interfaces, BCI)系统中，共同空间模式(Common Spatial Patterns, CSP)方法作为一种有效的处理方法被广泛使用。本程序实现了基本的CSP算法。-failed to translate
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Size: 3072 |
Author: 黄淦 |
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Description: BBC开发的DVB-T2系统的matlab通用仿真平台，编码的每步结果都输出在特定的TXT文件，可以用作结果验证！-BBC developed the DVB-T2 system Matlab simulation platform, every step of the coding results are output in a specific TXT file can be used for results validation.
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Size: 677888 |
Author: 王晋 |
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Description: The files are contained all knowledge!-The files are contained all knowledge!
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Size: 3072 |
Author: wzy616611 |
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Description: This the matlab code of common spatial pattern with tests.-This is the matlab code of common spatial pattern with tests.
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Size: 3072 |
Author: Nigel Lai |
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Description: This the matlab code of Regularized Common Spatial Pattern (R-CSP) algorithm.-This is the matlab code of Regularized Common Spatial Pattern (R-CSP) algorithm.
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Size: 2267136 |
Author: Nigel Lai |
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Description: CSP共空间模式的matlab代码实现，自己编写的CSP函数，测试好用，分享给初学者-CSP matlab code writed by myself,share with freshman.
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Size: 1024 |
Author: 汪海强 |
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Description: document about matlab code solar power
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Size: 222208 |
Author: guerrout |
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Description: 用csp对脑电信号就行特征提取的matlab代码-EEG with csp line feature extraction matlab code
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Size: 13148160 |
Author: 刘海娜 |
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Description: CSP特征提取算法，可用于两类特征的数据分类中(CSP feature extraction algorithm)
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Size: 6144 |
Author: xinjianli
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Description: 本MATLAB 例程用于脑电信号处理采用 CSP算法。(This MATLAB demo using CSP is intended for EEG signal processing)
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Size: 2111488 |
Author: zero_null |
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Description: 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上进行了实验测试，提出了最佳的DNN框架，准确率达到83.6%。(In this study, our goal was to use deep learning methods to improve the classification performance of motor imagery EEG signals. Therefore, we propose a classification method based on deep learning for motor imagery EEG signals. Based on the pre-processed raw EEG signals, a co-space model (CSP) method is used to extract the EEG feature matrix, which is then fed to a deep neural network (DNN) for training and classification. Our work was tested experimentally on the BCI Competition II Dataset III dataset, and the best DNN framework was proposed, achieving an accuracy of 83.6%.)
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Size: 14833664 |
Author: 渔舟唱晚1 |
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