【摘要】 针对维度情感模型生理信号情绪识别准确率较低的问题,本文基于DEAP维度情绪生理数据集,利用AR模型功率谱估计方法,提取脑电θ,α,β,γ节律的功率谱密度;采用小波包分解提取脑电小波包系数和能量占比时频特征;通过非线性分析提取脑电样本熵和小波包熵特征.然后,设计栈式自编码神经网络算法对脑电组合特征在效价和唤醒度两个情感维度上进行机器情绪识别.最后,分析了脑电特征、数据均衡以及情感标签对情绪识别结果的影响.仿真结果表明,栈式自编码神经网络用于脑电信号情绪识别的有效性,在情绪效价维度上,脑电情绪平均识别正确率可达80.3%;在唤醒度上,平均识别正确率达81.5%.该研究可为连续维度情绪自动分析和机器识别提供实际应用借鉴.更多还原

【Abstract】 To improve accuracy of emotional recognition for dimensional model on the physiological signals, based on the DEAP dimension emotional physiological data set, this paper uses the AR model power spectrum estimation method to extract the power spectrum density of the EEG θ, α, β and γ rhythms, uses wavelet packet decomposition to extract the EEG wavelet packet coefficients and energy ratio time-frequency characteristics, and uses nonlinear analysis method to extract EEG sample entropy and wavelet packet entropy. A stacked auto-encoder neural network algorithm is designed to perform machine emotion recognition on the EEG combination features in two emotional dimensions: valence and arousal. The effects on emotion recognition results, including EEG features, sample balances and emotion tags are analyzed. The simulation tests show that the stacked auto-encoder neural network is effective for the dimensional emotion recognition of EEG. In the emotional valence dimension, the average recognition accuracy rate can reach 80.3%; and the average recognition accuracy rate reaches 81.5% in the arousal dimension. The research can provide a practical reference for the automatic analysis and machine recognition on continuous dimension emotions.更多还原