Petrantonakis Panagiotis C, Hadjileontiadis Leontios J
Signal Processing and Biomedical Technology Unit, Telecommunications Laboratory, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 541 24 Thessaloniki, Greece.
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):186-97. doi: 10.1109/TITB.2009.2034649. Epub 2009 Oct 23.
Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).
基于脑电图（EEG）的情感识别是情感计算领域中一个相对较新的领域，在诱导情感状态和提取特征方面存在具有挑战性的问题，以便实现最佳分类性能。本文提出了一种新颖的情感诱发和基于EEG的特征提取技术。具体而言，镜像神经元系统概念被用于通过模仿过程有效地促进情感诱导。此外，高阶过零（HOC）分析被用于特征提取方案，并实现了一种稳健的分类方法，即HOC情感分类器（HOC-EC），测试了四种不同的分类器[二次判别分析（QDA）、k近邻、马氏距离和支持向量机（SVM）]，以实现高效的情感识别。通过一系列面部表情图像投影，16名健康受试者仅使用3个EEG通道（即根据10-20系统的Fp1、Fp2以及F3和F4位置的双极通道）收集了EEG数据。分别使用来自单通道和组合通道的EEG数据研究了两种情况。与其他特征提取方法相比，HOC-EC似乎表现更优，在单通道和组合通道情况下分别实现了62.3%（使用QDA）和83.33%（使用SVM）的分类准确率，能够区分六种基本情绪，即快乐、惊讶、愤怒、恐惧、厌恶和悲伤。随着情感类别集维度的降低，HOC-EC趋向于最大分类率（对于五种或更少情绪为100%），证明了所提方法的有效性。这可以促进HOC-EC在人机接口（如普及型医疗保健系统）中的集成，增强其情感特性并提供有关用户情感状态的信息（例如，识别用户的情感体验、反复出现的情感状态、随时间变化的情感趋势）。
