EVALUATION OF FEATURE EXTRACTION AND CLASSIFICATION TECHNIQUES FOR EEG-BASED SUBJECT IDENTIFICATION
DOI:
https://doi.org/10.11113/jt.v78.9717Keywords:
Subject Identification, Power Spectral Density, Kernel Density Estimation, Mel Frequency Cepstral Coefficients, Multilayer Perceptron, Naive Bayesian, Support Vector MachineAbstract
The ability to identify a subject is indispensable in affective computing research due to its wide range of applications. User profiling was created based on the strength of emotional patterns of the subject, which can be used for subject identification. Such system is made based on the emotional states of happiness and sadness, indicated by the electroencephalogram (EEG) data. In this paper, we examine several techniques used for subject profiling or identification purposed. Those techniques include feature extraction and classification techniques. In the experimental study, we compare three techniques for feature extraction namely, Power Spectral Density (PSD), Kernel Density Estimation (KDE), and Mel Frequency Cepstral Coefficients (MFCC). As for classification we compare three classification techniques, they are; Multilayer Perceptron (MLP), Naive Bayesian (NB), and Support Vector Machine (SVM). The best result achieved was 59.66%, using the MFCC and MLP-based techniques using 5-fold cross validation. The experiment results indicated that these profiles could be more accurate in identifying subject compared to NB and SVM. The comparisons demonstrated that profile-based methods for subject identification provide a viable and simple alternative to this problem.
References
P. Nguyen, D. Tran, X. Huang, and D. Sharma. 2012. A proposed Feature Extraction Method for EEG-based Person Identification. Int. Conf. Artif. Intell.
G. Lee, M. Kwon, S. Kavuri Sri, and M. Lee. 2014. Emotion Recognition Based on 3D Fuzzy Visual and EEG Features in Movie Clips. Neurocomputing. 144: 560-568.
D. Nie, X. Wang, L. Shi, and B. Lu. 2011. EEG-based Emotion Recognition during Watching Movies. Proc. 5th Int. IEEE EMBS Conf. Neural Eng. 667-670.
Y. Lin, C. Wang, T. Jung, T.-L. Wu, S.-K. Jeng, J.-R. Duann, and J.-H. Chen. 2010. EEG-Based Emotion Recognition in Music Listening. IEEE Trans. Biomed. Eng. 57(7): 1798-1806.
S. Koelstra and I. Patras. 2013. Fusion Of Facial Expressions And EEG For Implicit Affective Tagging. Image Vis. Comput. 31(2): 164-174.
S. Koelstra, A. Yazdani, M. Soleymani, C. Mühl, J.-S. Lee, A. Nijholt, T. Pun, T. Ebrahimi, and I. Patras. 2010. Single Trial Classification Of EEG And Peripheral Physiological Signals For Recognition Of Emotions Induced By Music Videos. Brain informatics. 89-100.
H. Jiang, G. Yang, X. Gui, N. Wu, and T. Zhang. 2012. Emotion Recognition System Design Using Multi-phsyological Signals. Proc. 11th IEEE Int. Conf. Cogn. Informatics Cogn. Comput.
M. Li and B. Lu. 2009. Emotion Classification Based on Gamma-band EEG. 31st Annu. Int. Conf. IEEE EMBS. 1323-1326.
Q. Zhang and M. Lee. 2009. Analysis Of Positive And Negative Emotions In Natural Scene Using Brain Activity And GIST. Neurocomputing. 72(4-6): 1302-1306.
J. Broekens and W.-P. Brinkman. 2013. AffectButton: A method for Reliable and Valid Affective Self-report. Int. J. Hum. Comput. Stud. 71(6): 641-667.
H. J. Yoon and S. Y. Chung. 2013. EEG-based Emotion Estimation Using Bayesian Weighted-log-posterior Function and Perceptron Convergence Algorithm. Comput. Biol. Med. 43(12): 2230-7.
K. C. Tseng, B.-S. Lin, C.-M. Han, and P.-S. Wang. 2013. Emotion Recognition of EEG Underlying Favourite Music by Support Vector Machine. 2013 Int. Conf. Orange Technol. 155-158.
N. Fuad and M. N. Taib. 2014. Three Dimensional EEG Model and Analysis of Correlation between Sub Band for Right and Left Frontal Brainwave for Brain Balancing Application. J. Mach. to Mach. Commun. 1: 91-106.
M. Soleymani, M. Pantic, and T. Pun. 2012. Multimodal Emotion Recognition in Response to Videos. IEEE Trans. Affect. Comput. 3(2): 211-223.
H. Yaacob, W. Abdul, and N. Kamaruddin. 2013. Classification of EEG Signals Using MLP based on Categorical and Dimensional Perceptions of Emotions. 2013 5th Int. Conf. Inf. Commun. Technol. Muslim World. 1-6.
R. Khosrowabadi, A. Wahab, K. K. Ang, and M. H. Baniasad. 2009. Affective Computation on EEG Correlates of Emotion from Musical and Vocal Stimuli. Proceeding Int. Jt. Conf. Neural Networks, Atlanta, Georg. USA. 1590-1594.
O. AlZoubi, R. A. Calvo, and R. H. Stevens. 2009. Classification of EEG for Affect Recognition: An Adaptive Approach. AI 2009 Adv. Artif. Intell. Lect. Notes Comput. Sci.. 5866: 52-61.
M. Othman, A. Wahab, I. Karim, M. A. Dzulkifli, and I. F. T. Alshaikli. 2013. EEG Emotion Recognition Based on the Dimensional Models of Emotions. Procedia - Soc. Behav. Sci.. 97: 30-37.
M. Molavi, J. Bin Yunus, and E. Akbari. 2012. Comparison of Different Methods for Emotion Classification. 2012 Sixth Asia Model. Symp. 50-53.
H. Yaacob and I. Karim. 2012. Two Dimensional Affective State Distribution Of The Brain Under Emotion Stimuli. 2012 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 6052-6055.
R. Aquino, J. Battad, C. Ngo, and G. Uy. 2012. Towards Empathic Support Provision for Computer Users. Theory Pract. Comput. Proc. Inf. Commun. Technol. 5: 15-27.
E. T. Mampusti, J. S. Ng, J. J. I. Quinto, G. L. Teng, M. T. C. Suarez, and R. S. Trogo. 2011. Measuring Academic Affective States of Students via Brainwave Signals. 2011 Third Int. Conf. Knowl. Syst. Eng. 226-231.
J. Kumar, B. Hegde, B. Deeksha, and N. Cauvery. 2015. Real-Time EEG Based Object Recognition. IJITR. 5-10.
P. C. Petrantonakis and L. J. Hadjileontiadis. 2009. EEG-based Emotion Recognition Using Hybrid Filtering and Higher Order Crossings. 2009 3rd Int. Conf. Affect. Comput. Intell. Interact. Work. 1-6.
K. Schaaff and T. Schultz. 2009. Towards an EEG-based Emotion Recognizer for Humanoid Robots. RO-MAN 2009 - 18th IEEE Int. Symp. Robot Hum. Interact. Commun. 792-796.
T. F. Bastos-Filho, A. Ferreira, A. C. Atencio, S. Arjunan, and D. Kumar. 2012. Evaluation of Feature Extraction Techniques in Emotional State Recognition. 2012 4th Int. Conf. Intell. Hum. Comput. Interact. 1-6.
M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic. 2012. A Multimodal Database for Affect Recognition and Implicit Tagging. IEEE Trans. Affect. Comput. 3(1): 42-55.
P. C. Petrantonakis and L. J. Hadjileontiadis. 2010. Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis. IEEE Trans. Affect. Comput. 1(2): 81-97.
P. J. Lang, M. M. Bradley, and B. N. Cuthbert. 1997. International Affective Picture System (IAPS): Technical Manual And Affective Ratings. NIMH Cent. Study Emot. Atten.
C. M. Krause, V. Viemerö, A. Rosenqvist, L. Sillanmäki, and T. Åström. 2000. Relative Electroencephalographic Desynchronization And Synchronization In Humans To Emotional Film Content: An Analysis Of The 4–6, 6–8, 8–10 And 10–12 Hz Frequency Bands. Neurosci. Lett. 286(1): 9-12.
J. Maroco, D. Silva, A. Rodrigues, M. Guerreiro, I. Santana, and A. de Mendonça. 2011. Data Mining Methods in The Prediction of Dementia: A real-data Comparison of the Accuracy, Sensitivity and Specificity of Linear Discriminant Analysis, Logistic Regression, Neural Networks, Support Vector Machines, Classification Trees and Random Forests. BMC Res. Notes. 4: 299.
C. Cortes and V. Vapnik. 1995. Support-vector Networks. Mach. Learn. 20(3): 273–297.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.