基于SAE和GNDO-SVM的脑电信号情绪识别

情感计算是现代人机交互中的关键问题, 随着人工智能的发展, 基于脑电信号(electroencephalogram, EEG)的情绪识别已经成为重要的研究方向. 为了提高情绪识别的分类精度, 本研究引入堆叠自动编码器(stacked auto-encoder, SAE)对EEG多通道信号进行深度特征提取, 并提出一种基于广义正态分布优化的支持向量机(generalized normal distribution optimization based support vector machine, GNDO-SVM)情绪识别模型. 实验结果表明, 与基于遗传算法、粒子群算法和麻雀搜索算法优化的支持向量机模型相比, 所提出的GNDO-SVM模型具有更优的分类性能, 基于SAE深度特征的情感识别准确率达到了90.94%, 表明SAE能够有效地挖掘EEG信号不同通道间的深度相关性信息. 因此, 利用SAE深度特征结合GNDO-SVM模型可以有效地实现EEG信号的情绪识别.

Emotion Recognition of EEG Signals Based on SAE and GNDO-SVM

Affective computing is a key problem in modern human-computer interaction, and with the development of artificial intelligence, emotion recognition based on electroencephalogram (EEG) has become an important research direction. To improve the classification accuracy of emotion recognition, this study introduces stacked auto-encoder (SAE) to extract the deep feature of EEG multichannel signals and then proposes a generalized normal distribution optimization based support vector machine (GNDO-SVM). The experimental results show that the proposed GNDO-SVM model has better classification performance than the support vector machine model optimized by genetic algorithm, particle swarm optimization algorithm, and sparrow search algorithm. The accuracy of emotion recognition based on SAE depth features reaches 90.94%, indicating that SAE can effectively exploit the depth correlation information between different channels of EEG signals. Therefore, applying SAE depth feature extraction combined with the GNDO-SVM classification model can effectively achieve the emotion recognition of EEG signals.