Affective computing
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Emotions from text: machine learning for text-based emotion prediction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Towards affective learning with an EEG feedback approach
MTDL '09 Proceedings of the first ACM international workshop on Multimedia technologies for distance learning
Characterization of EEG-A comparative study
Computer Methods and Programs in Biomedicine
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Emotion recognition from EEG using higher order crossings
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
A real-time automated system for the recognition of human facial expressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Technology in Biomedicine
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Recently, emotion classification from EEG data has attracted much attention with the rapid development of dry electrode techniques, machine learning algorithms, and various real-world applications of brain-computer interface for normal people. Until now, however, researchers had little understanding of the details of relationship between different emotional states and various EEG features. To improve the accuracy of EEG-based emotion classification and visualize the changes of emotional states with time, this paper systematically compares three kinds of existing EEG features for emotion classification, introduces an efficient feature smoothing method for removing the noise unrelated to emotion task, and proposes a simple approach to tracking the trajectory of emotion changes with manifold learning. To examine the effectiveness of these methods introduced in this paper, we design a movie induction experiment that spontaneously leads subjects to real emotional states and collect an EEG data set of six subjects. From experimental results on our EEG data set, we found that (a) power spectrum feature is superior to other two kinds of features; (b) a linear dynamic system based feature smoothing method can significantly improve emotion classification accuracy; and (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning.