Open Access
Issue
ITM Web Conf.
Volume 11, 2017
2017 International Conference on Information Science and Technology (IST 2017)
Article Number 07006
Number of page(s) 10
Section Session VII: Control and Automation
DOI https://doi.org/10.1051/itmconf/20171107006
Published online 23 May 2017
  1. Dalgleish T, Dunn B D, Mobbs D. Affective Neuroscience: Past, Present, and Future[J]. Emotion Review, 2009, 1(4):355–368. [CrossRef] [Google Scholar]
  2. Healey J. Wearable and automotive systems for the recognition of affect from physiology[D]. Massachusetts: MIT, 2000. [Google Scholar]
  3. Davidson R J. Anterior electrophysiological asymmetries, emotion, and depression: conceptual and methodological conundrums[J]. Psychophysiology, 1998, 35(5):607–614. [CrossRef] [Google Scholar]
  4. Baumgartner T, Esslen M, Jäncke L. From emotion perception to emotion experience: Emotions evoked by pictures and classical music[J]. International Journal of Psychophysiology, 2006, 60(1):34–43. [CrossRef] [Google Scholar]
  5. Gupta R, Laghari K U R, Falk T H. Relevance vector classifier decision fusion and EEG graph-theoretic features for automatic affective state characterization[J]. Neurocomputing, 2016, 174:875–884. [CrossRef] [Google Scholar]
  6. Jie X, Cao R, Li L. Emotion recognition based on the sample entropy of EEG[J]. Bio-medical materials and engineering, 2014, 24(1):1185–1192. [Google Scholar]
  7. Zheng W L, Lu B L. Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks[J]. IEEE Transactions on Autonomous Mental Development, 2015, 7(3):162–1175. [CrossRef] [Google Scholar]
  8. Duan R N, Zhu J Y, Lu B L. Differential entropy feature for EEG-based emotion classification[C]// International IEEE/EMBS Conference on Neural Engineering. IEEE, 2013:81–84. [Google Scholar]
  9. Yoon H J, Chung S Y. EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm[J]. Computers in Biology & Medicine, 2013, 43(12):2230–2237. [CrossRef] [Google Scholar]
  10. Sepideh H, Keivan M, Motie N A. The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals[J]. Journal of Medical Signals & Sensors, 2014, 4(3):194–201. [Google Scholar]
  11. Palus M. Nonlinearity in normal human EEG: cycles, temporal asymmetry, nonstationarity and randomness, not chaos[J]. Biological Cybernetics, 1996, 75(5):389–396. [CrossRef] [Google Scholar]
  12. Salmeron J L. Fuzzy cognitive maps for artificial emotions forecasting[J]. Applied Soft Computing, 2012, 12(12):3704–3710. [CrossRef] [Google Scholar]
  13. Lisa Feldman Barrett. Discrete Emotions or Dimensions? The Role of Valence Focus and Arousal Focus [J]. Cognition and Emotion, 1998, 12(4):579–599. [CrossRef] [Google Scholar]
  14. Robinson M D. Measures of emotion: A review[J]. Cognition and Emotion, 2009, 23(2):209–237. [CrossRef] [Google Scholar]
  15. Koelstra S, Muhl C, Soleymani M, et al. DEAP: A Database for Emotion Analysis Using Physiological Signals[J]. IEEE Transactions on Affective Computing, 2012, 3(1):18–31. [CrossRef] [Google Scholar]
  16. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J]. Journal of Neuroscience Methods, 2004, 134(1):9–21. [CrossRef] [Google Scholar]
  17. Andrew A M. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods [M]. Printed in the United Kingdom at the University Press, 2000. [Google Scholar]
  18. Liu C, Rani P, Sarkar N. An empirical study of machine learning techniques for affect recognition in human-robot interaction[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005:2662–2667. [Google Scholar]
  19. Chang C C, Lin C J. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems & Technology, 2011, 2(3):27. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  20. Horlings R, Datcu D, Rothkrantz L J M. Emotion recognition using brain activity[C]// International Conference on Computer Systems and Technologies and Workshop for Phd Students in Computing. ACM, 2008:6. [Google Scholar]
  21. Stuss D T. Functions of the frontal lobes: relation to executive functions[J]. Journal of the International Neuropsychological Society, 2011, 17(5):759–765. [CrossRef] [Google Scholar]
  22. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis[J]. Brain Research Reviews, 1999, 29(2-3):169–195. [CrossRef] [PubMed] [Google Scholar]
  23. Oathes D J, Ray W J, Yamasaki A S, et al. Worry, Generalized Anxiety Disorder, and Emotion: Evidence from the EEG Gamma Band[J]. Biological Psychology, 2008, 79(2):165–170. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.