基于精神状态的脑电个人特征与人脑机接口特征的关系研究

S. Ito, Y. Mitsukura, Katsuya Sato, S. Fujisawa, M. Fukumi
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引用次数: 2

摘要

本文以自我图模式为基础,讨论了听歌时的脑电图模式分类结果与反映人的个性特征的人的本性的关系。EEG分析计算EEG信号频率的功率谱,根据theta、alpha和beta节奏划分频带,并通过EEG模式分类来评估音乐是否与用户的情绪相匹配。采用k近邻分类器对脑电模式进行分类。自我图是用来探测人类本性的。最后,讨论了脑电模式分类结果与人的本性的关系。一个有趣的发现是,当被试被归类为具有内向型的自我图模式时,其对负刺激反应的脑电图模式的识别准确率更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study on relationship between personal feature of EEG and human's characteristic for BCI based on mental state
This paper discusses the relationship the result classified the electroencephalogram (EEG) patterns while listening to music and the human's nature, which indicates the personal feature of a human, based on the egogram pattern. The EEG analysis calculates the power spectra of the frequency of the EEG signal, divides into the frequency bands based on theta, alpha, and beta rhythms, and evaluates whether the music matches mood of the user or not through EEG pattern classification. A k-nearest neighbor classifier is used to classify the EEG patterns. The egogram is used for detecting nature of the human. Finally, we discuss the relationship the result of EEG pattern classification and the human's nature. An interesting finding was that the recognition accuracy of the EEG pattern meaning the response of them on negative stimuli became high when the subject was classified into the egogram pattern with introverted nature.
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