Peningkatan Akurasi Pengenalan Emosi pada Sinyal Electroencephalograpy Menggunakan Multiclass Fisher Discriminant Analysis

Evi Septiana Pane, Adhi Dharma Wibawa, Mauridhi Hery Purnomo


EEG signals have a significant correlation to emotions when compared to other external appearances such as face and voice. Due to the low accuracy of emotional recognition through EEG signals, this study proposes a dimensional reduction method for EEG data to address that problem using Multiclass Fisher Discriminant Analysis (MC-FDA). In this study, the experiment was applied on public EEG dataset with three classes of emotions, namely positive, negative, and neutral. Differential entropy features were extracted from the decomposed EEG signals in five frequency band of the delta, theta, alpha, beta, and gamma. The accuracy of emotion recognition was measured using two prevalent classifiers on EEG identification, such as LDA and SVM. To demonstrate the superiority of the MC-FDA method, the PCA dimension reduction method was applied as a comparison. Classification accuracy results from all experiment scenario showed the advantages of the MC-FDA compared to the PCA. The best emotion classification accuracy was obtained from trials on all data from twelve electrodes using the MC-FDA and LDA methods, namely 93.3%. These results show a mean increase in accuracy of 3.5 points from the original feature vector dataset.


gelombang otak, reduksi dimensi, EEG, pengenalan emosi multikelas

Full Text:



K.B. Koh, “Emotion and Immunity,” Journal of Psychosomatic Research, Vol. 45, No. 2, hal. 107–115, 1998.

J.F. Brosschot dan J.F. Thayer, “Heart Rate Response is Longer After Negative Emotions than After Positive Emotions,” International Journal of Psychophysiology, Vol. 50, No. 3, hal. 181–187, Nov. 2003.

C. Busso, Z. Deng, S. Yildirim, M. Bulut, C.M. Lee, A. Kazemzadeh, S. Lee, U. Neumann, dan S. Narayanan, “Analysis of Emotion Recognition Using Facial Expressions, Speech and Multimodal Information,” Proceedings of the 6th international conference on Multimodal interfaces, 2004, hal. 205–211.

S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, dan I. Patras, “DEAP: A Database for Emotion Analysis; Using Physiological Signals,” IEEE Transactions on Affective Computing, Vol. 3, No. 1, hal. 18–31, 2012.

A.M. Bhatti, M. Majid, S.M. Anwar, and B. Khan, “Human Emotion Recognition and Analysis in Response to Audio Music Using Brain Signals,” Computers in Human Behavior, Vol. 65, hal. 267–275, Dec. 2016.

C. Mühl, B. Allison, A. Nijholt, dan G. Chanel, “A Survey of Affective Brain Computer Interfaces: Principles, State-of-the-Art, and Challenges,” Brain-Computer Interfaces, Vol. 1, No. 2, hal. 66–84, 2014.

W.-L. Zheng dan B.-L. Lu, “Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks,” IEEE Transactions on Autonomous Mental Development, Vol. 7, No. 3, hal. 162–175, 2015.

I. T. Jolliffe dan J. Cadima, “Principal Component Analysis: A Review And Recent Developments,” Phil. Trans. R. Soc. A, Vol. 374, No. 2065, hal. 20150202, 2016.

A. Subasi dan M.I. Gursoy, “EEG Signal Classification Using PCA, ICA, LDA and Support Vector Machines,” Expert Systems with Applications, Vol. 37, No. 12, hal. 8659–8666, Dec. 2010.

F. Artoni, A. Delorme, dan S. Makeig, “Applying Dimension Reduction to EEG Data by Principal Component Analysis Reduces the Quality of Its Subsequent Independent Component Decomposition,” NeuroImage, Vol. 175, hal. 176–187, 2018.

A. M. Martínez dan A.C. Kak, “PCA Versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, hal. 228–233, 2001.

M. Welling, “Fisher Linear Discriminant Analysis,” University of Toronto, Technical Note, 2005.

P. Xanthopoulos, P.M. Pardalos, dan T.B. Trafalis, “Linear Discriminant Analysis,” in Robust Data Mining, New York, USA: Springer, 2013, hal. 27–33.

Y.-H. Liu, W.-T. Cheng, Y.-T. Hsiao, C.-T. Wu, dan M.-D. Jeng, “EEG-based Emotion Recognition Based on Kernel Fisher’s Discriminant Analysis and Spectral Powers,” 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2014, pp. 2221–2225.

W.-L. Zheng, J.-Y. Zhu, dan B.-L. Lu, “Identifying Stable Patterns over Time for Emotion Recognition from EEG,” IEEE Transactions on Affective Computing, pp. 1–1, 2017.

R. Jenke, A. Peer, dan M. Buss, “Feature Extraction and Selection for Emotion Recognition from EEG,” IEEE Transactions on Affective Computing, Vol. 5, No. 3, hal. 327–339, Jul. 2014.

R. Darmakusuma, A.S. Prihatmanto, A. Indrayanto, dan T.L. Mengko, “Deteksi Intensi Pergerakan Jari Menggunakan Metode Power Spectral Density dengan Stimulus Visual,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol. 4, No. 2, hal. 125-129. Dec. 2015.

A. Surtono, T.S. Widodo, and M. Tjokronagoro, “Analisis Klasifikasi Sinyal EKG Berbasis Wavelet dan Jaringan Syaraf Tiruan,” JNTETI, vol. 1, no. 3, hal. 60-66, 2012.

R.-N. Duan, J.-Y. Zhu, dan B.-L. Lu, “Differential Entropy Feature for EEG-based Emotion Classification,” 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013, hal. 81–84.

H. Anton, Elementary Linear Algebra, New York: Wiley, 1987.

I.H. Witten, E. Frank, M.A. Hall, dan C.J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Burlington, USA: Morgan Kaufmann, 2016.

J. Atkinson dan D. Campos, “Improving BCI-based Emotion Recognition by Combining EEG Feature Selection and Kernel Classifiers,” Expert Systems with Applications, Vol. 47, hal. 35–41, Apr. 2016.

X.-W. Wang, D. Nie, dan B.-L. Lu, “Emotional State Classification From EEG Data Using Machine Learning Approach,” Neurocomputing, Vol. 129, hal. 94–106, Apr. 2014.

Y.-P. Lin, C.-H. Wang, T.-P. Jung, T.-L. Wu, S.-K. Jeng, J.-R. Duann, dan J.-H. Chen, “EEG-Based Emotion Recognition in Music Listening,” IEEE Transactions on Biomedical Engineering, Vol. 57, No. 7, hal. 1798–1806, Jul. 2010.

C. Cortes, dan V. Vapnik, “Support-Vector Networks,” Machine Learning, Vol. 20, No. 3, hal. 273–297, 1995.

J. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines,” Microsoft Research, Technical Report MSR-TR-98-14, hal. 1-21, 1998.

W. Zheng, “Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis,” IEEE Transactions on Cognitive and Developmental Systems, Vol. 9, No. 3, hal. 281–290, Sep. 2017.



  • There are currently no refbacks.

Copyright (c) 2018 Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

JNTETI (Jurnal Nasional Teknik Elektro dan Teknologi Informasi)

Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik Universitas Gadjah Mada
Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
+62 274 552305