Wavelet Packet-Based Feature Extraction for Brain-Computer Interfaces

机译：基于小波包的脑机接口特征提取

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A novel feature extraction method of spontaneous electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is explored. The method takes the wavelet packet transform (WPT) as an analysis tool and utilizes two kinds of information. Firstly, EEG signals are transformed into wavelet packet coefficients by the WPT. And then average coefficient values and average power values of certain subbands are computed, which form initial features. Finally, part of average coefficient values and part of average power values with larger Fisher indexes are combined to form the feature vector. Compared with previous feature extraction methods, the proposed approach can lead to higher classification accuracy.

机译：探索了一种新颖的特征提取方法，用于脑 - 计算机接口（BCIS）的自发脑电图（EEG）信号。该方法将小波包变换（WPT）作为分析工具，并利用两种信息。首先，eEG信号由WPT转换为小波分组系数。然后计算某些子带的平均系数值和平均功率值，其形成初始特征。最后，平均系数值的一部分和具有较大Fisher索引的平均功率值的一部分组合以形成特征向量。与先前的特征提取方法相比，所提出的方法可以导致更高的分类精度。

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《International conference on life system modeling and simulation;International conference on intelligent computing for sustainable energy and environment;LSMS 2010;ICSEE 2010》|2010年|p.19-26|共8页
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Banghua Yang; Li Liu; Peng Zan; Wenyu Lu;
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正文语种
中图分类生物工程学（生物技术）;人工智能理论;
关键词
brain-computer interface (BCI); electroencephalogram (EEG); feature extraction; wavelet packet transform (WPT);

机译：脑机接口（BCI）;脑电图（EEG）;特征提取;小波包变换（WPT）;

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1. Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces [J] . Bang-hua Yang, Guo-zheng Yan, Ting Wu, Signal processing . 2007,第7期

机译：人机界面中基于模糊小波包的基于主题的特征提取
2. Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition. [J] . Yang BH, Yan GZ, Yan RG, Medical engineering & physics. . 2007,第1期

机译：使用小波包最佳基分解的脑机接口中基于主题的自适应特征提取。
3. Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network [J] . Si Yue, Zhang Zhousuo, Kong Chuiqing, Structural health monitoring . 2020,第3期

机译：基于双树复小波包的深度自动化网络松动性条件提取粘弹性三明治结构
4. Wavelet Packet-Based Feature Extraction for Brain-Computer Interfaces [C] . Banghua Yang, Li Liu, Peng Zan, International Conference on Life System Modeling and Simulation . 2010

机译：基于小波包的脑电脑接口特征提取
5. On-line learning and wavelet-based feature extraction methodology for process monitoring using high-dimensional functional data. [D] . Omitaomu, Olufemi Abayomi. 2006

机译：使用高维功能数据进行过程监控的在线学习和基于小波的特征提取方法。
6. Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces [O] . Keum-Shik Hong, M. Jawad Khan, Melissa J. Hong 2018

机译：混合fNIRS-EEG脑机接口的特征提取和分类方法
7. Feature Selection and Feature Extraction Approaches to P300 Detection in On-line Brain-Computer Interface [O] . Chumerin Nikolay, Manyakov Nikolay V., Combaz Adrien, 2009

机译：在线脑机界面中P300检测的特征选择和特征提取方法

Wavelet Packet-Based Feature Extraction for Brain-Computer Interfaces

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