Using N-Grams of Quantized EEG Values for Happiness Detection

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• 关键词：EEG ; N ; grams ; Happiness detection ; Classification
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9703
• 期：1
• 页码：270-279
• 全文大小：1,012 KB
• 参考文献：1.Wang, X.-W., Dan Nie, B.L.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129(1), 94–106 (2014)CrossRef
  2.Jatupaiboon, N., Pan-ngum, S., Israsena, P.: Real-time EEG-based happiness detection system. Sci. World J. 2013, 52–61 (2013)CrossRef
  3.Lee, Y.-Y., Hsieh, S.: Classifying different emotional states by means of EEG-basedfunctional connectivity patterns. PLoS ONE 9(4), e95415 (2014)CrossRef
  4.Chanel, G., Kierkels, J.J., Soleymani, M., Pun, T.: Short-term emotion assessment in a recall paradigm. Int. J. Hum. Comput. Stud. 67(8), 607–627 (2009)CrossRef
  5.Soleymani, M., Chanel, G., Kierkels, J.J.M., Pun, T.: Affective characterization of movie scenes based on content analysis and physiological changes. Int. J. Semant. Comput. 3(2), 235–254 (2009)CrossRef
  6.Niedermeyer, E., da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 5th edn. Lippincott Williams & Wilkins, Baltimore (2004)
  7.Tatum, W.O.: Ellen r. grass lecture: Extraordinary EEG. Neurodiagnostic J. 54, 3–21 (2014)
  8.Cahn, B.R., Polich, J.: Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol. Bull. 132(2), 180–211 (2006)CrossRef
  9.Millet, D.: The origins of EEG. In: Seventh Annual Meeting of the International Society for the History of the Neurosciences (ISHN), Los Angeles, California, USA Department of Neurology, UCLA Medical Center (2004)
  10.Pfurtscheller, G., de Lopes, F.H.: Event-related EEG/MEG synchronization and desynchronization:basic principles. Clin. Neurophysiol. 110(11), 1842–1857 (1999)CrossRef
  11.Fawcett, T.: ROC graphs: Notes and practical considerations for researchers. Technical report, HP Labs (2004)
  
• 作者单位：David Pinto (18)
  Darnes Vilariño (18)
  Illiana Morales (18)
  Cristina Aguilar (18)
  Mireya Tovar (18)
  
  18. Faculty of Computer Science Language and Knowledge Engineering Lab, Benemérita Universidad Autonóma de Puebla, Puebla, Mexico
  
• 丛书名：Pattern Recognition
• ISBN：978-3-319-39393-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9703

文摘

When applying classification methods for the automatic detection of happiness in human beings using electroencephalographic signals, the major research works in literature report the employment of power spectral density as the main feature. However, the aim of this paper is to explore wheter or not the use of N-grams of quantized EEG values as new features may help to improve the classification process. N-grams is a standard method of data representation in the area of natural language processing which usually reports good results. In this type of input data make sense to employ this kind of representation because the happiness signal is made up of a sequence of values which naturally matches the N-grams paradigm. The results obtained show that this kind of representation obtains better results than others reported in literature.