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基于脑电信号的情绪识别

Emotion recognition based on electroencephalographsignals

作者: 李贤哲  暴伟  谢能刚  
单位:安徽工业大学管理科学与工程学院(安徽马鞍山 243002) <p>通信作者:谢能刚,教授。E-mail:xienenggang@aliyun.com</p> <p>&nbsp;</p>
关键词: 脑电;视频实验设计;特征提取;情绪识别  
分类号:R318.04 <p>&nbsp;</p>
出版年·卷·期(页码):2022·41·1(8-16)
摘要:

目的 对多种情绪进行快速准确的识别,是目前脑机接口和情感计算领域的研究热点。本文针对多情绪分类问题及个体差异的影响因素,设计一种电影片段诱发实验,利用机器学习算法对9位被试不同情绪的脑电信号进行分析,以期能够快速准确识别不同被试的情绪状态。方法 首先采用非侵入式脑电设备收集被试在恐惧、愤怒、悲伤和快乐4种情绪下的脑电信号,通过对信号进行降噪处理,使用时域、频域和非线性动力学的特征提取方法,共提取出15种不同的有效特征,并根据均方根特征和三维时域特征的散点图来验证4种情绪之间的区分性;最后以平均准确率作为分类识别的评价指标,应用K近邻算法对9位被试整体的脑电特征进行训练和分类。结果 3种时域特征的识别率差异较大,一阶差分绝对值的均值特征平均准确率达到95%,其余时域特征分类效果一般;频域特征中使用Welch法得到Gamma频带特征识别效果最好,平均准确率超过95%;非线性动力学特征识别率较好,平均分类准确率都超过90%。结论 利用Welch法得到的Gamma频带特征和一阶差分绝对值的均值作为最优特征,能够快速准确识别不同被试的情绪状态。

 

Objective Rapid and accurate recognition of multiple emotions is becoming a research hot spot in the field of brain-computer interface and affective computing currently. This paper focuses on a variety of emotional classification problems and the factors that cause individual differences, We design a kind of film segment inducing experiment. It can use machine learning algorithms to analysis the electroencephalograph(EEG) signals of 9 participants with different emotions and identify the emotional state of different participants quickly and accurately. Methods Firstly, it uses the non-invasive EEG equipment to collect the participants' EEG signals under the four emotions of fear, anger, sadness and happiness. Then,  it uses time domain, frequency domain and nonlinear dynamic feature extraction methods to extract a total of 15 different effective features through the signal noise reduction processing. According to the root mean square feature and the scatter plot of the three-dimensional time domain feature, it can verify the distinction between the four emotions. Finally, it takes the average accuracy as the evaluation index of classification and recognition. Meanwhile, the k-nearest neighbor algorithm is applied to train and classify the overall EEG features of 9 participants. Results The recognition rates of the three time-domain features are quite different, the average accuracy of mean value of absolute value of first order difference features reaches 95%, but the classification effect of other time-domain features is general. In the frequency domain features, the Gamma frequency band feature recognition effect obtained by using the Welch method is the best, with an average accuracy rate exceeding 95%. The recognition rate of nonlinear dynamic features is good, and the average classification accuracy rate exceeds 90%. Conclusions Using the Gamma frequency band feature obtained by Welch method and the average accuracy of mean value of absolute value as the optimal feature, it can identify the emotional state of different participants quickly and accurately.

 

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