Emotion recognition based on EEG features in movie clips with channel selection

Petrrushin V (1999) Emotion in speech: recognition and application to call centers. In: Processing of the artificial networks in engineering conference, pp 7–10

Anderson K, McOwan P (2006) A real-time automated system for the recognition of human facial expression. IEEE Trans Syst Man Cybern B Cybern 36:96–105

Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69–87

Atyabi A, Luerssen MH, Powers DMW (2013) PSO-based dimension reduction of EEG recordings: implications for subject transfer in BCI. Neurocomputing 119(7):319–331

Petrantonokis PC, Hadjileontiadis LJ (2010) Emotion recognition from EEG using higher order crossing. IEEE Trans Inf Technol Biomed 14(2):186–197

Khosrowbadi R, Quek HC, Wahab A, Ang KK (2010) EEG based emotion recognition using self-organizing map for boundary detection. In: International conference on pattern recognition, pp 4242–4245

Torres-Valencia C, Garcia-Arias HF, Alvarez Lopez M, Orozco-Gutierrez A (2014) Comparative analysis of physiological signals and electroencephalogram (EEG) for multimodal emotion recognition using generative models. In: 19th symposium on image, signal processing and artificial vision, Armenia-Quindio

Russell JA (1980) A circumplex model of affect. J Personal Soc Psychol 39:1161–1178

Wang XW, Nie D, Lu BL (2014) Emotional state classification from EEG data using machine learning approach. Neurocomputing 129:94–106

Kim J, Andre E (2006) Emotion recognition using physiological and speech signal in short-term observation. In: proceedings of the perception and interactive technologies, 4021:53–64

Brosschot J, Thayer J (2006) Heart rate response is longer after negative emotions than after positive emotions. Int J Psychophysiol 50:181–187

Kim K, Bang S, Kom S (2004) Emotion recognition system using short-term monitoring of physiological signals. Med Biol Eng Comput 42:419–427

Subaşı A, Erçelebi E (2005) Classification of EEG signals using neural network and logistic regression. Comput Methods Progr Biomed 78:87–99

Subası A (2007) Signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32:1084–1093

Fu K, Qu J, Chai YDY (2014) Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed Signal Process Control 13:15–22

Lopetegui E, Zapirain BG, Mendez A (2011) Tennis computer game with brain control using EEG signals. In: The 16th international conference on computer games, pp 228–234

Leeb R, Lancelle M, Kaiser V, Fellner DW, Pfurtscheller G (2013) Thinking Penguin: multimodal brain computer interface control of a VR game. IEEE Trans Comput Intell AI in Games 5(2):117–128

Murugappan M, Ramachandran N, Sazali Y (2010) Classification of human emotion from EEG using discrete wavelet transform. J. Biomed Sci Eng 3:390–396

Cahnel G, Kroneeg J, Grandjean D, Pun T (2005) Emotion assesstment: arousal evaluation using EEG’s and peripheral physiological signals, 24 rue du genaral dufour, Geneva

Zhang Q, Lee M (2009) Analysis of positive and negative emotions in natural scene using brain activity and GIST. Neurocomputing 72:1302–1306

Bahrdwaj A, Gupta A, Jain P, Rani A, Yadav J (2015) Classification of human emotions from EEG signals using SVM and LDA classifiers. In: 2nd international conference on signal processing and integrated networks (SPIN), pp 180–185

Lee G, Kwon M, Sri SK, Lee M (2014) Emotion recognition based on 3D fuzzy visual and EEG features in movie clips. Neurocomputing 144:560–568

DEAP: a dataset for emotion analysis EEG physiological and video signals (2012) http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html . Accessed 01 May 2015

Koelstra S, Mühl C, Soleymani M, Lee J, Yazdani A, Ebrahimi T, Pun T, Nijholt A, Patras I (2012) DEAP: a database for emotion analysis using physiological signals. IEEE Trans Affect Comput 3(1):18–31

Bradley MM, Lang PJ (1994) Measuring emotions: the self-assessment manikin and the sematic differential. J Behav Ther Exp Psychiatry 25(1):49–59

Uusberg A, Thiruchselvam R, Gross J (2014) Using distraction to regulate emotion: insights from EEG theta dynamics. Int J Psychophysiol 91:254–260

Polat H, Ozerdem MS (2015) Reflection emotions based on different stories onto EEG signal. In: 23th conference on signal processing and communications applications, Malatya, pp 2618–2618

Kıymık MK, Akın M, Subaşı A (2004) Automatic recognition of alertness level by using wavelet transform and artificial neural network. J Neurosci Methods 139:231–240

Amato F, Lopez A, Mendez EMP, Vanhara P, Hampl A (2013) Artificial neural networks in medical diagnosis. J Appl Biomed 11:47–58

Haykin S (2009) Neural networks and learning machines, 3rd edn. Prentice Hall, New Jersey, p 906

Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals computing design and application. J Microbiol Methods 43:3–31

Patnaik LM, Manyam OK (2008) Epileptic EEG detection using neural networks and post-classification. Comput Methods Progr Biomed 91:100–109

Berrueta LA, Alonso RM, Heberger K (2007) Supervised pattern recognition in food analysis. J Chromatogr A 1158:196–214

Atkinson J, Campos D (2016) Improving BCI–based emotion recognition by combining EEG feature selection and kernel classifiers. Expert Syst Appl 47:35–41