Open Access Open Access  Restricted Access Subscription or Fee Access

Emotion Recognition using EEG Signals

Babasab Gadade, Dr. N. K. Cauvery

Abstract


Emotion is the affective state and the complex experience of consciousness. An Emotion is characterized by the impulse of brain neuron. Brain Computer Interface is the technology that builds the communicates between human brain and computer system. The research on BCI applications is an emerging field that encourages to work with human brain and helps to analyze the human brain functionalities based on the state of the brain. This paper recognizes four different emotions using DEAP EEG data. The Fast Fourier Transformation is used to transfer the EEG data from time domain to frequency domain. Bandpass filter 4 – 45 Hz is used to extract alpha, beta, gamma and theta frequencies. The power of the frequency band is calculated and features are extracted. The Relief-F algorithm is used to select the highest weight features. The Ten-fold cross validation technique is used to train and test the classifier. The classifier Probabilistic Neural Network is used to classify the four different emotions namely, Arousal, Valence, Dominance and Liking. The average accuracy of the classifier for each emotion is 85% to 92%.


Keywords


EEG (Electroencephalography); PNN (Probabilistic Neural Network); Emotion Recognition; KNN (K-Nearest Neighbor) – Feature Selection.

Full Text:

PDF

References


Jianhai Zhang, Yu Cao, Robert Kozma, “PNN for EEG-Based Emotion Recognition”, IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, October 9-12, 2016.

Merzagora, A.C., Bunce, S., Izzetoglu, M. and Onaral, B. (2006) Wavelet analysis for EEG feature extraction in deceptive detection. IEEE Proceedings on EBMS, 5, 6, 1179-1200.

Murugappan, M., Rizon, M., Nagarajan, R. and Yaacob, S. (2009) An Investigation on visual and audiovisual stimulus based emotion recognition using EEG. Transactions on Medical Engineering and Informatics, 1(3), 225-229.

Mallat, S.G. (1989) A theory for multi-resolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 574-583.

E. Niedermeyer, F. H. Lopes da Silva. 1993. Electroencephalography: Basic principles, clinical applications and related fields, 3rd edition, Lippincott, Williams & Wilkins, Philadelphia.

Anbumani A, Sathishkumar A. Emotion analysis from physiological signal using EEG. International Journal of Engineering Sciences and Management Research. 2014 Jul; 1(2):27–31.

Murugappan, M., Rizon, M., Nagarajan, R. and Yaacob, S. (2009a) Inferring of human emotion states using multichannel EEG. International Journal of Soft Computing and Applications (IJSCA), EURO Journals, United Kingdom.

Chanel, G., Kronegg, J., Grandjean, D. and Pun, T. (2005) Emotion assessment: Arousal evaluation using EEG’s and peripheral physiological signals. Technical Report, 520-531.

I. B. Mauss and M. D. Robinson, “Measures of emotion: A review,” Cognition & Emotion, vol. 23, no. 2, 2009.

S. Koelstra, C. Muhl, M. Soleymani, J. S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt and I. Patras, "DEAP: A Database for Emotion Analysis Using Physiological Signals," IEEE Transactions on Affective Computing, vol. 3, pp. 11-21, 2011.

Y. H. Gulhane1, S. A. Ladhake2 “ A Short Survey on Methodology for Stress Recognition” , International Journal of Emerging Research in Management Technology ISSN: 2278-9359 (Volume-5, Issue-12)

N. Jatupaiboon, S. Pan-Ngum and P. Israsena, "Emotion classification using minimal EEG channels and frequency bands," in International Joint Conference on Computer Science and Software Engineering, 2013, pp. 51-54.

M. Sarlo, G. Buodo, S. Poli, and D. Palomba, ”Changes in EEG alpha power to different disgust elicitors: the specificity of mutilations,” Neuroscience Letters,vol.382,no.3,pp.271-276,2005

D. Nie, X. W. Wang, L. C. Shi, and B. L. Lu, "EEG-based emotion recognition during watching movies," in International IEEE/EMBS Conference on Neural Engineering, 2011.

R. Jenke, A. Peer and M. Buss, "Feature Extraction and Selection for Emotion Recognition from EEG," IEEE Transactions on Affective Computing, vol. 5, pp. 337-339, 2014.

Frequency Domain Features and Support Vector Machines," in Neural Information Processing - International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, 2011.

A. Heraz, R. Razaki, and C. Frasson, “Using machine learning to predict learner emotional state from brain waves,” in Proc. 7th IEEE Int. Conf. Adv. Learning Technol., 2007, pp. 853-857.

K. Ishino and M. Hagiwara, “A feeling estimation system using a simple electroencephalograph,” in Proc. IEEE Int. Conf. Syst. Man Cybern., 2003, vol. 5, pp. 4206-4209.

EEG Model and Location in Brain when at Emotion Recognition System Using Brain and Peripheral Signals using Correlation Dimension to Improve the Results of EEG: Z. Khalili , M. H. Moradi Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran. 978-1-4244- 3553, 2009 IEEE.

K. Schaaff and T. Schultz, "Towards emotion recognition from EEG signals," in Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on, 2009.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.