Description: 该程序是用小波函数构建神经网络的源程序。用以分析心电信号、脑电信号等等。-that the procedure was constructed using wavelet neural network function of the source. For the analysis of ECG, EEG, and so on. Platform: |
Size: 1451 |
Author:sandy4000 |
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Description: 该程序是用小波函数构建神经网络的源程序。用以分析心电信号、脑电信号等等。-that the procedure was constructed using wavelet neural network function of the source. For the analysis of ECG, EEG, and so on. Platform: |
Size: 1024 |
Author:sandy4000 |
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Description: This program is to one-step EEG prediction. it is done by a fuzzy neural network based on a chaotic back propagation training method. Platform: |
Size: 6144 |
Author:Mehran Ahmadlou |
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Description: This program is prepared as an one-step EEG predictor. this is used a fuzzy neural network which is trained by a chaotic back propagation method Platform: |
Size: 197632 |
Author:Mehran Ahmadlou |
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Description: 脑电信号是脑神经细胞电生理活动在大脑皮层或头皮表面的总体反映 ,脑电信号的研究
一直是生物医学领域难度很大且倍受人们关注的课题。在简要回顾了脑电研究的历史和现状的基础上 ,重点论述了混沌分析法、人工神经网络(ANN)分析法、小波变换法、Wigner 分布等在脑电信号分析和处理中的应用情况。最后展望了脑电信号研究的发展应用前景。-EEG is a brain cell electrophysiological activity in the cerebral cortex or the scalp surface, the overall reflection of the study EEG bio-medical field has always been a very difficult and oft-topic of concern. After a brief review of the history and current status of EEG research, based on the analysis focuses on the chaos, artificial neural network (ANN) analysis, wavelet transform, Wigner distribution in the brain signal analysis and processing of the application. Finally, the future research and development of EEG application. Platform: |
Size: 101376 |
Author:还都 |
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Description: 本文详细阐述了小波神经网络在脑电信号数据压缩与棘波识别中的应用-This paper describes the neural network in EEG data compression and spikes Recognition Platform: |
Size: 246784 |
Author:gt |
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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: 从脑电信号的分析出发,论述了频域分析、时域分析等脑电图分析中常用的信号分析方法和特点,特别介绍了Wigner分布、小波变换和匹配跟踪等时频分析方法、人工神经网络和非线性动力学方法在脑电信号分析和处理中的应用情况。
-From the analysis of eeg, discusses the frequency domain and time domain analysis analysis in the analysis of the commonly used eeg signal analysis method and characteristics, especially introduced the Wigner distribution, wavelet transform and matching trace the method of time-frequency analysis, artificial neural network and nonlinear dynamics method of eeg analysis and processing in the application. Platform: |
Size: 32768 |
Author:lvxin |
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Description: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model Platform: |
Size: 2897920 |
Author:ALi |
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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%.) Platform: |
Size: 14833664 |
Author:渔舟唱晚1 |
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