[论文解读] Affective State Recognition through EEG Signals Feature Level Fusion and Ensemble Classifier

Md. Mahbubur Rahman, Akash Poddar+2 more|arXiv (Cornell University)|Feb 14, 2021

Emotion and Mood Recognition参考文献 11被引用 5

一句话总结

本研究提出了一种基于脑电信号（EEG）的新型情感状态识别框架，通过特征级融合与集成分类器实现。在情绪视频刺激下，通过融合EEG数据的统计特征与高级谱特征，该框架在分类四种基本情感状态（快乐、悲伤、厌恶与平静）时达到了89.06%的准确率，其中随机森林在集成分类器中表现最佳。

ABSTRACT

Human affects are complex paradox and an active research domain in affective computing. Affects are traditionally determined through a self-report based psychometric questionnaire or through facial expression recognition. However, few state-of-the-arts pieces of research have shown the possibilities of recognizing human affects from psychophysiological and neurological signals. In this article, electroencephalogram (EEG) signals are used to recognize human affects. The electroencephalogram (EEG) of 100 participants are collected where they are given to watch one-minute video stimuli to induce different affective states. The videos with emotional tags have a variety range of affects including happy, sad, disgust, and peaceful. The experimental stimuli are collected and analyzed intensively. The interrelationship between the EEG signal frequencies and the ratings given by the participants are taken into consideration for classifying affective states. Advanced feature extraction techniques are applied along with the statistical features to prepare a fused feature vector of affective state recognition. Factor analysis methods are also applied to select discriminative features. Finally, several popular supervised machine learning classifier is applied to recognize different affective states from the discriminative feature vector. Based on the experiment, the designed random forest classifier produces 89.06% accuracy in classifying four basic affective states.

研究动机与目标

• 开发一种基于非侵入性EEG信号的稳健情感状态识别系统。
• 通过利用神经生理信号，解决自评与面部表情方法的局限性。
• 通过先进的特征融合与集成学习技术，提升分类准确率。
• 识别与主观情感评分相关的EEG特征。

提出的方法

• 在100名参与者观看一分钟情绪标签视频刺激时采集EEG信号。
• 从EEG信号中提取统计特征与谱特征（如δ、θ、α、β、γ频带的功率）。
• 特征级融合将多种特征类型整合为单一判别性特征向量。
• 应用因子分析以降低维度并选择最具信息量的特征。
• 评估多种监督机器学习分类器，最终选择随机森林作为集成模型。
• 基于训练好的集成分类器，利用融合后的特征向量对情感状态进行分类。

实验结果

研究问题

• RQ1 EEG信号能否可靠地捕捉并区分基本情感状态，如快乐、悲伤、厌恶与平静？
• RQ2 统计与谱EEG特征的特征级融合在提升情感状态分类准确率方面效果如何？
• RQ3 在从EEG数据中识别多种情感状态时，哪种集成分类器表现最佳？
• RQ4 EEG频带之间的相互关系与主观情感评分的关联在多大程度上提升了分类性能？

主要发现

• 所提出的特征级融合方法显著优于单一特征集的分类性能。
• 随机森林分类器在区分四种基本情感状态时达到了最高的89.06%准确率。
• 因子分析成功降低了特征维度，同时保留了判别性信息。
• α与β频带的谱功率与主观情感评分表现出强相关性。
• 集成模型在多种情感状态中表现出稳健性与良好的泛化能力。
• 本研究证实了利用EEG信号实现实时、非侵入性人机交互情感状态识别的可行性。