EEG-based human emotion recognition using entropy as a feature extraction measure

Scherer KR (2005) What are emotions? And how can they be measured? Soc Sci Inf 44:695–729

Vijayan AE, Sen D, Sudheer AP (2015) EEG-based emotion recognition using statistical measures and auto-regressive modeling. In: 2015 IEEE international conference on computational intelligence & communication technology. pp 587–591

Li X, Song D, Zhang P et al (2018) Exploring EEG features in cross-subject emotion recognition. Front Neurosci 12:162

Picard RW, Vyzas E, Healey J (2001) Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 23:1175–1191

Jerritta S, Murugappan M, Nagarajan R, Wan K (2011) Physiological signals based human emotion recognition: a review. In: 2011 IEEE 7th international colloquium on signal processing and its applications. pp 410–415

Liu Y, Sourina O, Nguyen MK (2010) Real-time EEG-based human emotion recognition and visualization. In: 2010 international conference on cyberworlds. pp 262–269

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

Ang J, Dhillon R, Krupski A et al (2002) Prosody-based automatic detection of annoyance and frustration in human–computer dialog. In: Seventh international conference on spoken language processing. pp 2037–2040

Herwig U, Satrapi P, Schönfeldt-Lecuona C (2003) Using the international 10–20 EEG system for positioning of transcranial magnetic stimulation. Brain Topogr 16:95–99

Pirbhulal S, Zhang H, Wu W et al (2018) Heartbeats based biometric random binary sequences generation to secure wireless body sensor networks. IEEE Trans Biomed Eng 65:2751–2759

Pirbhulal S, Zhang H, Mukhopadhyay SC et al (2015) An efficient biometric-based algorithm using heart rate variability for securing body sensor networks. Sensors 15:15067–15089

Li M, Lu B-L (2009) Emotion classification based on gamma-band EEG. In: 2009 annual international conference of the IEEE engineering in medicine and biology society. pp 1223–1226

Wu W, Pirbhulal S, Sangaiah AK et al (2018) Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Gener Comput Syst 86:515–526

Pirbhulal S, Zhang H, Alahi E, ME, et al (2017) A novel secure IoT-based smart home automation system using a wireless sensor network. Sensors 17:69

Sodhro AH, Pirbhulal S, Qaraqe M et al (2018) Power control algorithms for media transmission in remote healthcare systems. IEEE Access 6:42384–42393

Acharya UR, Sree SV, Swapna G et al (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165

Hosseini SA, Naghibi-Sistani MB (2011) Emotion recognition method using entropy analysis of EEG signals. Int J Image Graph Signal Process 3:30

Oberman LM, McCleery JP, Ramachandran VS, Pineda JA (2007) EEG evidence for mirror neuron activity during the observation of human and robot actions: toward an analysis of the human qualities of interactive robots. Neurocomputing 70:2194–2203

Wang Q, Sourina O, Nguyen MK (2010) EEG-based "serious" games design for medical applications. In: 2010 international conference on cyberworlds. pp 270–276

Thatcher RW, Budzynski T, Budzynski H et al (2009) EEG evaluation of traumatic brain injury and EEG biofeedback treatment. In: Introduction to quantitative EEG and neurofeedback: advanced theory and applications. pp 269–294

Pandiyan PM, Yaacob S et al (2013) Mental stress level classification using eigenvector features and principal component analysis. Commun Inf Sci Manag Eng 3:254

Aftanas LI, Reva NV, Varlamov AA et al (2004) Analysis of evoked EEG synchronization and desynchronization in conditions of emotional activation in humans: temporal and topographic characteristics. Neurosci Behav Physiol 34:859–867

Hadjidimitriou SK, Hadjileontiadis LJ (2012) Toward an EEG-based recognition of music liking using time–frequency analysis. IEEE Trans Biomed Eng 59:3498–3510

Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Comput 5:327–339

Lay-Ekuakille A, Vergallo P, Griffo G et al (2013) Entropy index in quantitative EEG measurement for diagnosis accuracy. IEEE Trans Instrum Meas 63:1440–1450

Lu Y, Wang M, Wu W et al (2020) Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals. Measurement 150:107003

Duan R-N, Zhu J-Y, Lu B-L (2013) Differential entropy feature for EEG-based emotion classification. In: 2013 6th international IEEE/EMBS conference on neural engineering (NER). pp 81–84

Xiang J, Li C, Li H et al (2015) The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 243:18–25

Chen T, Ju S, Yuan X et al (2018) Emotion recognition using empirical mode decomposition and approximation entropy. Comput Electr Eng 72:383–392

Strongman KT (1996) The psychology of emotion: theories of emotion in perspective. Wiley, New York

Ekman P, Friesen WV, O’sullivan M et al (1987) Universals and cultural differences in the judgments of facial expressions of emotion. J Pers Soc Psychol 53:712

Lang PJ (1995) The emotion probe: studies of motivation and attention. Am Psychol 50:372

Peter C, Herbon A (2006) Emotion representation and physiology assignments in digital systems. Interact Comput 18:139–170

Kim J, André E (2008) Emotion recognition based on physiological changes in music listening. IEEE Trans Pattern Anal Mach Intell 30:2067–2083

Rani P, Liu C, Sarkar N, Vanman E (2006) An empirical study of machine learning techniques for affect recognition in human–robot interaction. Pattern Anal Appl 9:58–69

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

Rigas G, Katsis CD, Ganiatsas G, Fotiadis DI (2007) A user independent, biosignal based, emotion recognition method. In: Conati C, McCoy K, Paliouras G (eds) User modeling 2007. Springer, Berlin, pp 314–318

Haag A, Goronzy S, Schaich P, Williams J (2004) Emotion recognition using bio-sensors: first steps towards an automatic system. In: André E, Dybkjær L, Minker W, Heisterkamp P (eds) Affective dialogue systems. Springer, Berlin, pp 36–48

Gross JJ, Levenson RW (1995) Emotion elicitation using films. Cogn Emot 9:87–108

Koelstra S, Muhl C, Soleymani M et al (2011) Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3:18–31

Zheng W-L, Liu W, Lu Y et al (2018) Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern 49:1110–1122

Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Circ Physiol 278:H2039–H2049

Tong J, Liu S, Ke Y et al (2017) EEG-based emotion recognition using nonlinear feature. In: 2017 IEEE 8th international conference on awareness science and technology (iCAST). pp 55–59

Candra H, Yuwono M, Chai R et al (2015) Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). pp 7250–7253

Rosso OA, Blanco S, Yordanova J et al (2001) Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods 105:65–75

Ho KKL, Moody GB, Peng C-K et al (1997) Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96:842–848

Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88:2297–2301

Voss A, Baier V, Schulz S, Bar KJ (2006) Linear and nonlinear methods for analyses of cardiovascular variability in bipolar disorders. Bipolar Disord 8:441–452

Ryan SM, Goldberger AL, Pincus SM et al (1994) Gender-and age-related differences in heart rate dynamics: are women more complex than men? J Am Coll Cardiol 24:1700–1707

Seely AJE, Macklem PT (2004) Complex systems and the technology of variability analysis. Crit Care 8:R367

Pincus S (1995) Approximate entropy (ApEn) as a complexity measure. Chaos Interdiscip J Nonlinear Sci 5:110–117

Farmer JD (1982) Information dimension and the probabilistic structure of chaos. Zeitschrift für Naturforsch A 37:1304–1326

Falniowski F (2014) On the connections of generalized entropies with Shannon and Kolmogorov-Sinai entropies. Entropy 16:3732–3753

Li X, Ouyang G, Richards DA (2007) Predictability analysis of absence seizures with permutation entropy. Epilepsy Res 77:70–74

Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88:174102

Riedl M, Müller A, Wessel N (2013) Practical considerations of permutation entropy. Eur Phys J Spec Top 222:249–262

Zanin M, Zunino L, Rosso OA, Papo D (2012) Permutation entropy and its main biomedical and econophysics applications: a review. Entropy 14:1553–1577

Fadlallah B, Chen B, Keil A, Principe J (2013) Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. Phys Rev E 87:22911

Kosko B (1986) Fuzzy entropy and conditioning. Inf Sci 40:165–174

Zhang XP, Fan YL, Yang Y (2009) On the classification of consciousness tasks based on the EEG singular spectrum entropy. Comput Eng Sci 31:117–120

Lotfalinezhad H, Maleki A (2019) Application of multiscale fuzzy entropy features for multilevel subject-dependent emotion recognition. Turkish J Electr Eng Comput Sci 27:4070–4081

Marwan N, Wessel N, Meyerfeldt U et al (2002) Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys Rev E 66:26702

Zhang Y, Ji X, Zhang S (2016) An approach to EEG-based emotion recognition using combined feature extraction method. Neurosci Lett 633:152–157

Yang Y-X, Gao Z-K, Wang X-M et al (2018) A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG. Chaos Interdiscip J Nonlinear Sci 28:85724

Goshvarpour A, Abbasi A, Goshvarpour A (2016) Recurrence quantification analysis and neural networks for emotional EEG classification. Appl Med Inform 38:13–24

Jie X, Cao R, Li L (2014) Emotion recognition based on the sample entropy of EEG. Biomed Mater Eng 24:1185–1192