|本期目录/Table of Contents|

[1]徐 昊,黄 敏,周昌乐*.用于冥想神经反馈系统的脑电图数据挖掘研究[J].厦门大学学报(自然科学版),2018,57(02):258-264.[doi:10.6043/j.issn.0438-0479.201710005]
 XU Hao,HUANG Min,ZHOU Changle*.Studies on Electroencephalograph Data Mining for Meditation Neurofeedback Systems[J].Journal of Xiamen University(Natural Science),2018,57(02):258-264.[doi:10.6043/j.issn.0438-0479.201710005]
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用于冥想神经反馈系统的脑电图数据挖掘研究(PDF)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年02期
页码:
258-264
栏目:
研究论文
出版日期:
2018-03-31

文章信息/Info

Title:
Studies on Electroencephalograph Data Mining for Meditation Neurofeedback Systems
文章编号:
0438-0479(2018)02-0258-07
作者:
徐 昊1黄 敏12周昌乐1*
1.厦门大学 信息科学与技术学院,福建省类脑计算技术及应用重点实验室,2.厦门大学人文学院,福建 厦门 361005
Author(s):
XU Hao1HUANG Min12ZHOU Changle1*
1.Fujian Key Lab of Brain-like Computation Technology and Application,School of Information Science and Engineering,Xiamen University,2.College of Humanities,Xiamen University,Xiamen 361005,China
关键词:
脑电图 冥想 数据挖掘 机器学习 神经反馈 个性化校准 禅修
Keywords:
electroencephalograph meditation data mining machine learning neurofeedback individualized calibration Zen
分类号:
TP 391.1
DOI:
10.6043/j.issn.0438-0479.201710005
文献标志码:
A
摘要:
有研究表明禅修冥想有益于现代人的身心健康,为了研发能有助于禅修的冥想神经反馈系统,采集乐易心法七日禅学员脑电图信号,提取15维脑电图特征,以禅修导师给学员的评分作为标记,对数据进行个性化校准,测试多种分类和回归算法对学员水平的分类性能.实验结果表明,个性化校准方案可以有效解决脑电图研究中的个体差异问题,15维脑电图特征数据经校准后可以使随机森林等分类算法以93%以上的准确率识别出高水平禅修者,为更加智能的冥想神经反馈系统的研发提供了支持.
Abstract:
Meditation benefits the health of modern people.In order to develop smart neurofeedback systems which help the Zen practice,several electroencephalograph indexes and three advanced analyzing methods are used to classify good or bad new Zen practitioners based on the evaluation made by Zen master.Individualized calibration processes are conducted.Results indicate that 15-dimensional EEG feature and individualized calibration process can effectively solve problems of individual difference of EEG and the accuracy of the classification algorithms can achieve higher than 93% for identifying good meditation practice.Results support the development of smarter meditation neurofeedback systems.

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备注/Memo

备注/Memo:
收稿日期:2017-10-09 录用日期:2018-01-17
基金项目:国家自然科学基金(61673322,61573294)
*通信作者:dozero@xmu.edu.cn
引文格式:徐昊,黄敏,周昌乐.用于冥想神经反馈系统的脑电图数据挖掘研究[J].厦门大学学报(自然科学版),2018,57(2):258-264.
Citation:XU H,HUANG M,ZHOU C L.Study on electroencephalograph(EEG)data mining for meditation neurofeedback systems[J].J Xiamen Univ Nat Sci,2018,57(2):258-264.(in Chinese)
更新日期/Last Update: 1900-01-01