| MSI和CCA算法对稳态视觉诱发电位信号分类的比较研究 |
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| 引用本文： | 高诺,翟文文,杨玉娜.MSI和CCA算法对稳态视觉诱发电位信号分类的比较研究[J].信号处理,2018,34(8):984-990. |
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| 作者姓名： | 高诺 翟文文 杨玉娜 |
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| 作者单位： | 山东建筑大学信息与电气工程学院 |
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| 基金项目： | 2015山东省科技重大专项（2015ZDXX0801A03）; 2017山东省重点研发计划（2017CXGC1505） |
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| 摘 要： | 脑机接口（Brain Computer Interface, BCI）系统能让那些有运动障碍的病人用脑信号与外界设备交互。稳态视觉诱发电位（Steady State Visual Evoked Potential，SSVEP）具有分析正确率高，不用训练等优点而倍受重视。如何高效地对SSVEP信号频率识别是SSVEP-BCI的关键问题，并关系到BCI的系统优劣。本文采用多变量同步指数与典型相关分析方法对SSVEP信号分类进行比较研究，探讨了两种方法在数据长度、导联数量、导联位置以及参考信号的谐波数量对SSVEP信号分类效果的影响。六位被试者参与实验采集数据，实验结果证实，在时间窗较小，数据长度较少的条件下，多变量同步指数方法较典型相关分析方法性能更优。而对于SSVEP信号分析来说，导联位置的准确性是影响频率分析算法的最根本因素。
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| 关 键 词： | 多变量同步指数 典型相关分析 视觉稳态诱发电位 正确率 脑电 |
| 收稿时间： | 2018-05-07 |
A comparison of MSI and CCA for SSVEP classification |
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| Affiliation: | School of Information and Electrical Engineering, Shandong Jianzhu University |
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| Abstract: | The Brain Computer Interface (BCI) system allows those with dyskinesia to control the device using only brain signals. At present, Steady State Visual Evoked Potential (SSVEP) is highly valued because of its high accuracy of analysis and lack of training. How to effectively identify the SSVEP signal frequency is the key issue of SSVEP-BCI, and it is related to the advantages and disadvantages of the BCI system. In this paper, multivariate synchronization index and canonical correlation analysis methods are used to compare SSVEP signal classification, and the effects of the two methods on the SSVEP signal classification effect are discussed in terms of data length, number of leads, lead position, and number of harmonics of the reference signal. Six subjects participated in the experiment. The experimental results show that the multivariate synchronization index method performs better than the typical correlation analysis method when the time window is small and the data length is small. For SSVEP signal analysis, the accuracy of the lead position is the most fundamental factor affecting the frequency analysis algorithm. |
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