Classification of Emotions Induced by Horror and Relaxing Movies Using Single-Channel EEG Recordings

Amir Jalilifard, Amir Rastegarnia, Ednaldo Birgante Pizzolato, Md Kafiul Islam

Abstract


It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations' patterns in Theta, Alpha and Beta sub-bands also play important role in brain's emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances\textemdash for each subject \textemdash were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208\% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when \begin{math} k=1 \end{math}) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for \begin{math} K >1 \end{math} in K-NN, SVM has better average classification rate.

Keywords


Electroencephalography; Fear; Relaxation; Single-channel; Emotion Classification; Stationary Wavelet; Short-Time Fourier Transform; EEG dynamics; K-Nearest Neighbors; Support Vector Machine.

References


R. Picard, Affective Computing. MIT Press, 2000.

K. Anderson and P. McOwan, A real-time automated system for the recognition of human facial expressions.

IEEE Trans. Syst., 2006, vol. 36.

T. Ruffman, J. D. Henry, V. Livingstone, and L. H., A meta-analytic review of emotion recognition and aging:

Implications for neuropsychological models of aging Neuroscience and Biobehavioral Reviews, 2008.

P. Petrantonakis and L. Hadjileontiadis, A novel emotion elicitation index using frontal brain asymmetry for

enhanced EEG-based emotion recognition. IEEE Trans. Inf. Technol, 2011.

J. A. Coan and J. J. B. Allen, Frontal EEG asymmetry as a moderator and mediator of emotion. Biological

Psychology, 2004.

X. Li, B. Hu, T. Zhu, J. Yan, and F. Zheng, Towards affective learning with an EEG feedback approach. in:

Proceedings of the 1st ACM International Workshop on Multimedia Technologies for Distance Learning, 2009.

R. J. Davidson, “What does the prefrontal cortex do in affect: perspectives on frontal EEG asymmetry research,”

Biological Psychology, vol. 67, 2004.

Y. Liu, O. Sourina, and M. K. Nguyen, “Real-time eeg-based emotion recognition and its applications,” in Transactions

on computational science XII. Springer, 2011, pp. 256–277.

N. Jatupaiboon, S. Pan-ngum, and P. Israsena, “Real-time eeg-based happiness detection system,” The Scientific

World Journal, vol. 2013, 2013.

D. BL, S. JS, S. SK, M. MA, C. CS, C. VI, H. J, and K. MA., “Electrophysiological spatiotemporal dynamics

during implicit visual threat processing,” Brain and Cognition, vol. 91, pp. 54–61, 2014.

S. M. P. G. Iacoviello D., Petracca A, “A real-time classification algorithm for eeg-based {BCI} driven

by self-induced emotions,” Computer Methods and Programs in Biomedicine, pp. Published on–line, 2015.

[Online]. Available: http://www.sciencedirect.com/science/article/pii/S0169260715002217

M. Naji, M. Firoozabadi, and P. Azadfallah, Emotion classification during music listening from forehead biosignal,

Signal, Image and Video Processing. DOI: 10, 2013.

R. E., G. D., C. T., and C. M., “Relationship between adult attachment patterns, emotional experience and EEG

frontal asymmetry,” Personality and Individual Differences, vol. 44, 2008.

L. Y. Y. and H. S., “Classifying different emotional states by means of EEG-based functional connectivity patterns,”

PLoS ONE. 2014;9(4) doi:, vol. 10., 2014.

J. A. Russell, A circumplex model of affect. Journal of Personality and Social Psyschology 39(6), 11611178,

University of British Columbia, Canada, 1980.

E. M. Mueller, C. Panitz, C. Hermann, and D. A. Pizzagalli, “Prefrontal Oscillations during Recall of Conditioned

and Extinguished Fear in Humans,” Journal of Neuroscience, vol. 34, pp. 7059–7066, 2014.

A. I. and K. M., “Arousal vs relaxation: a comparison of the neurophysiological and cognitive correlates of

vajrayana and theravada meditative practices.” PLoS ONE 9:e102990, vol. 10., 2014.

E. Keogh, A Gentle Introduction to Machine Learning and Data Mining for the Database Community, 2003.

L. J, X. J, R. I, V. A, M. GS et al., “Increased theta and alpha EEG activity,” J Altern Complement Med

:11871192. doi:, vol. 10., 2009.

J. GD and F. R., EEG spectral analysis of relaxation techniques. Applied Psychophysiology Biofeedback, 2004.

D. BR, H. JA, and M. WL, Concentration and mindfulness meditations: Unique forms of consciousness? Appl

Psychophysiol Biofeedback 24:147165, 1999.

C. BR, D. A, and P. J, Occipital gamma activation during Vipassana Meditation. Cogn Process 11:3956, 2010.

N. MM, K. JH, and T. DF, Prefrontal Oscillations during Recall of Conditioned and Extinguished Fear in Humans.

Biological Psychiatry, 2008, vol. 63.

E. A. Phelps and J. E. LeDoux, Contributions of the amygdala to emotion processing: From animal models to

human behavior. Neuron, 2005, vol. 48, no. 2.

X.-W. Wang, D. Nie, and B.-L. Lu, “Emotional state classification from eeg data using machine

learning approach,” Neurocomput., vol. 129, pp. 94–106, Apr. 2014. [Online]. Available:

http://dx.doi.org/10.1016/j.neucom.2013.06.046

S. P.-N. Suwicha Jirayucharoensak and P. Israsena, EEG-Based Emotion Recognition Using Deep Learning

Network with Principal Component Based Covariate Shift Adaptation. The Scientific World Journal, Sept

L. Mu and L. Bao-Liang, Emotion classification based on gamma-band EEG. Engineering in Medicine and

Biology Society, 2009.

V. Giurgiutiu and L. Yu, Comparison of Short-time Fourier Transform and Wavelet Transform of Transient and

Tone Burst Wave Propagation Signals For Structural Health Monitoring. 4th International Workshop on Structural

Health Monitoring, Stanford University, CA, Sept 2003.

S. Mallat, A wavelet tour of signal processing: the sparse way. Academic press, 2008.

H. Guo and C. S. Burrus, “Convolution using the undecimated discrete wavelet transform,” in icassp, 1996, pp.

–1294.

M. K. I. Molla, M. R. Islam, T. Tanaka, and T. M. Rutkowski, Artifact suppression from eeg signals using data

adaptive time domain filtering. Neurocomputing, 2004, vol. 97.

R. R. Coifman and D. L. Donoho, Translation-invariant denoising. Springer, 1995.

D. Safieddine, A. Kachenoura, L. Albera, G. Birot, A. Karfoul, A. Pasnicu, A. Biraben, F. Wendling, L. Senhadji,

and I. Merlet, Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and

deterministic (EMD and Wavelet-based) approaches. EURASIP Journal on Advances in Signal Processing,

S. A. Dudani, “The distance-weighted k-nearest neighbor rule,” IEEE Transactions on System, Man, and Cybernetics,

vol. 6, pp. 325–327, 1976.

V. A. K.P Soman, R. Loganathan, Machine Learning with SVM and Other Kernal Methods. Prentice-Hall of

India Pvt.Ltd, 2011.

J. K. Han and Micheline, Data mining: concepts and techniques. Morgan Kaufmann, 2011.

X. Q., Z. H., W. Y., and H. J., “Fuzzy support vector machine for classification of eeg signals using wavelet based

features,” Medical Engineering and Physics, vol. 31, pp. 858–865, 2009.

A. Ashari, I. Paryudi, and A. M. Tjoa, “Performance comparison between naive bayes,” Decision Tree and kNearest

Neighbor in Searching Alternative Design in an Energy Simulation Tool , (IJACSA) International Journal

of Advanced Computer Science and Applications, vol. 4, no. 11, 2013.

F. Colas and P. Brazdil, “Comparison of svm and some older classification algorithms in text classification tasks,”

pp. 169–178, 2006.

X. W. Wang, D. Nie, and B. L. Lu, Emotional state classification from EEG data using machine learning approach,

R. Yuvaraj, M. Murugappan, N. M. Ibrahim, K. Sundaraj, M. I. Omar, K. Mohamad, and R. Palaniappan, Optimal

set of EEG features for emotional state classification and trajectory visualization in Parkinson’s disease.

International Journal of Psychophysiology 94, 2014.

C. IC and F. BH, Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate

approach. Int J Psychophysiol 51:143153, 2004.

L. RW, H. K, E. P, and F. WV, Emotion and Autonomic Nervous-System Activity in the Minangkabau of West

Sumatra. J Pers Soc Psychol 62:972988, 1992.

D. . S. A. Chatterjee, D. ; Das, Cognitive load measurement - A methodology to compare low cost commercial

EEG devices. Advances in Computing, Communications and Informatics, 2014.

S. P.-n. Noppadon Jatupaiboon and P. Israsena, “Real-time eeg-based happiness detection system,” The Scientific

World Journal, vol. 2013, 2013.

M. K. Islam, A. Rastegarnia, A. T. Nguyen, and Z. Yang, “Artifact characterization and removal for in vivo neural

recording,” Journal of Neuroscience Methods, vol. 226, pp. 110–123, 2014.

M. K. Islam, A. Rastegarnia, and Z. Yang, “A wavelet-based artifact reduction from scalp EEG for epileptic

seizure detection,” 2015.

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Wiley, 2001.

M. A. . A. G. A. Hassanat, A. B. ; Abbadi and A. Alhasanat, Solving the Problem of the K Parameter in the KNN

Classifier Using an Ensemble Learning Approach. International Journal of Computer Science and Information

Security, 2014.

B. stn, W. J. Melssen, and L. M. C. Buydens, “Facilitating the application of Support Vector Regression by using

a universal Pearson VII function based kernel,” Chemometrics and Intelligent Laboratory Systems, vol. 81, pp.

–40, 2006.

P. Indyk and R. Motwani, “Approximate nearest neighbors: Towards removing the curse of dimensionality,”

, pp. 604–613.




DOI: http://doi.org/10.11591/ijece.v10i4.pp%25p
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