基于 SVM 新的情感计算方法

传统的情感计算方法主要基于面部表情和肢体动作的变化, 为解决此类方法存在的主观性问题, 以人在正性和负性情感下的 EEG(Electroencephalagrams)信号作为研究对象, 使用中国情感图片系统, 设计了测试者观看情感刺激图片的实验, 使用支持向量机算法对数据进行分析, 并根据实验的实际情况提出了将连续的12 条数据分为一组, 采用其均值进行计算的方法。 实验结果表明, 该方法的识别准确率达到 73. 33%。 这不仅提供了一种减少情感产生时延导致误差的方法, 同时也为脑电波的分析和优化提供了参考。

New Emotional Evaluate Method Based on SVM

Traditional emotional evaluate methods are mainly based on changes in facial expressions and body movements. To solve the subjective problems existing in these methods, we use positive and negative emotional EEG ( Electroencephalagrams) signal as the research object, anddesign the picture watching experiments with the help of the CAPS ( Chinese Affective Picture System) of Chinese Academy of Sciences, and then analyze the final data using machine learning algorithm of SVM ( Support Vector Machine ) . Moreover, according to the actual situation of the experiment, this paper presents a new method that dividing 12 consecutive data into one set and using the mean of each set into emotional evaluating. The results show that this new method obtainsa better recognition accuracy of 73 . 33%. This research not only provides a method that reduces the error caused by emotion generating delay, but also provides a reference for the analysis and optimization of brain waves.