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Description: Matlab source for blind classification of EEG data (BCI competition II data set IV)
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Size: 2823168 |
Author: sirali |
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Description: 人机接口技术：人机接口的目标是让机器为人服务，降低人的沟通难度，人机之间基于自然语言的智能化沟通将成为必然，由于人与人和人与机器的沟通方式没有差别，机器能够随时随地介入人的工作、生活中，帮助人们自动记录、整理资料。-BCI-finalreport
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Size: 5773312 |
Author: wangdognhai |
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Description: BCI2008竞赛的数据集说明，对下过该数据集的研究者帮助甚大，这也是国际最新的BCI竞赛数据-BCI2008 competition data set shows the data for the next set of researchers have contributed greatly, and this is the latest international BCI competition data
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Size: 198656 |
Author: yang |
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Description: BCI Competition 2008 Graz data set A格式说明-BCI Competition 2008 Graz data set A describtion
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Size: 198656 |
Author: fang |
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Description: BCI Competition IV Dataset 2b Submission by 脑机接口竞赛提交论文-BCI Competition IV Dataset 2b Submission by
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Size: 155648 |
Author: fang |
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Description: 2005BCI竞赛数据3 详细的BCI竞赛数据-2005BCI BCI Competition Data 3 detailed race data
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Size: 101376 |
Author: 庞雪 |
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Description: 脑机接口比赛的相关文件。主要适用于采用bci2000的应用软件编程的朋友。对学校bci技术有一定的帮助意义-Relevant documents of BCI competition. Mainly applied to software programming applications using bci2000 friends. Bci technical schools have some help significance
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Size: 1681408 |
Author: 赵宇 |
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Description: 用于分解提取BCI竞赛数据集，将四类想象运动的有效EEG信号分别提取到四个矩阵中。-BCI competition for decomposition to extract data sets, four types of effective motor imagery EEG signals were extracted to four matrix.
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Size: 8192 |
Author: 王中辉 |
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Description: BCI的2003年竞赛数据,p300的全部竞赛数据!(BCI's 2003 competition data, P300's all contest data!)
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Size: 18432 |
Author: zhulin40 |
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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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