An Electroencephalogram Analysis Method to Detect Preference Patterns Using Gray Association Degrees and Support Vector Machines
An Electroencephalogram Analysis Method to Detect Preference Patterns Using Gray Association Degrees and Support Vector Machines
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DOI：
10.25046/aj030514
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发表时间：
2018-09
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影响因子：
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通讯作者：
S. Ito;Momoyo Ito;M. Fukumi
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文献类型：
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作者：
S. Ito;Momoyo Ito;M. Fukumi
本文介绍了一种脑电(EEG)分析方法来检测特定声音的偏好。我们的研究旨在创造新型的脑机接口(BMI)来控制人类的心理(NBMICM)，它用于检测人类的心理状况，即偏好、思维和意识，选择刺激来控制这些心理状况，并对这些选择进行评估。检测对刺激的偏好是很重要的。如果可以检测到与偏好相关的刺激，则NBCIMC可以根据用户的情绪通过检测他们喜欢的刺激来向用户提供刺激。该方法采用了脑电信号记录技术、脑电信号特征提取技术和偏好检测方法。脑电记录采用简单的脑电图仪，测量位置是大脑的左额叶。我们假设，脑电活动在偏好模式上的差异表现为脑电各频段功率谱变化之间的关联。为了计算关联度，我们使用了灰色理论模型。通过计算灰色关联度来提取脑电信号特征，然后利用支持向量机进行参数检测。通过实验验证了该方法的有效性，实验结果表明，该方法对喜爱的声音检测的平均正确率为>；88%。这些结果表明，用灰色关联度作为脑电信号的特征，用支持向量机作为分类器，对脑电信号进行分析，就可以很容易地检测出受试者的喜好声音。
This paper introduces an electroencephalogram (EEG) analysis method to detect preferences for particular sounds. Our study aims to create novel brain–computer interfaces (BMIs) to control human mental (NBMICM), which are used to detect human mental conditions i.e., preferences, thinking, and consciousness, choose stimuli to control these mental conditions, and evaluate these choices. It is important to detect the preferences on stimuli. If the stimuli related to the preference can be detected, the NBCIMC can provide stimuli to the user based on their emotions by detecting their favorite stimuli. The proposed method adopted EEG recording technique, extraction techniques of EEG features and detection methods of preferences. EEG recording employs a simple electroencephalograph, for which the measurement position is the left frontal lobe (Fp1) of the brain. We assume that the differences of the EEG activities on the patterns of preference are expressed in the association between the changes of the power spectra on each frequency band of the EEG. To calculate the association, we employ the gray theory model. The EEG feature is extracted by calculating the gray association degree, then, the preferences are detect using a support vector machine (SVM). Experiments are conducted to test the effectiveness of this method, which is validated by a mean accuracy rate >88% on the favorite sound detection. These results suggest that the detection of subject’s favorite sounds becomes easy when the EEG signals are analyzed while the gray associate degrees are used as the EEG feature and the SVM is used as the classifier.