Modifikasi Fitur dengan Differential Asymmetry untuk Meningkatkan Akurasi Klasifikasi EEG Motor Imagery

Yulianto Tejo Putranto, Tri Arief Sardjono, Mochamad Hariadi, Mauridhi Hery Purnomo

Abstract


Brain-Computer Interface (BCI) technology has enabled people with motor disabilities to interact with their environment. The electroencephalograph (EEG) signals related to a motor imagery movement were used as a control signal. In this paper, EEG motor imagery signals from the 2-class data have been processed into features and classified. The power and standard deviation of EEG signals, mean of absolute wavelet coefficients, and the average power of the wavelet coefficients were used as features. The purpose of this paper is to apply the differential asymmetry of these features as new features to improve the system accuracy. As a classifier, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Tree were used. The result shows that for dataset I the use of differential asymmetry as feature can increase the system accuracy up to 47.8%, from 52.20% to 100%, with Tree as a classifier. For dataset II, it can increase accuracy by 8.46%, from 54.42% to 62.48%.

Keywords


Brain-Computer Interface; EEG motor imagery; differential asymmetry; SVM; k-NN; Tree

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References


L. Bougrain, M. Clerc, dan F. Lotte, Ed., Brain-Computer Interfaces. 1: Foundations and Methods, First published. London, UK: ISTE Ltd, 2016.

M. Bentlemsan, E.-T. Zemouri, D. Bouchaffra, B. Yahya-Zoubir, dan K. Ferroudji, “Random Forest and Filter Bank Common Spatial Patterns for EEG-Based Motor Imagery Classification,” 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation, 2014, hal. 235–238.

S.L. Wu, Y.T. Liu, T.Y. Hsieh, Y.Y. Lin, C.Y. Chen, C.H. Chuang, dan C.T. Lin, “Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain–Computer Interface,” IEEE Trans. Fuzzy Syst., Vol. 25, No. 1, hal. 21–28, Feb. 2017.

N. Tomida, T. Tanaka, S. Ono, M. Yamagishi, dan H. Higashi, “Active Data Selection for Motor Imagery EEG Classification,” IEEE Trans. Biomed. Eng., Vol. 62, No. 2, hal. 458–467, Feb. 2015.

S.-M. Park, X. Yu, P. Chum, W.-Y. Lee, dan K.-B. Sim, “Symmetrical Feature for Interpreting Motor Imagery EEG Signals in the Brain–Computer Interface,” Opt. - Int. J. Light Electron Opt., Vol. 129, hal. 163–171, Jan. 2017.

W.-Y. Hsu, “Motor Imagery Electroencephalogram Analysis Using Adaptive Neural-Fuzzy Classification,” Int. J. Fuzzy Syst., Vol. 16, No. 1, hal. 111-120, 2014.

M. Li, X. Luo, dan J. Yang, “Extracting the Nonlinear Features of Motor Imagery EEG Using Parametric t-SNE,” Neurocomputing, Vol. 218, hal. 371–381, Des. 2016.

S.U. Kumar dan H.H. Inbarani, “PSO-Based Feature Selection and Neighborhood Rough Set-Based Classification for BCI Multiclass Motor Imagery Task,” Neural Comput. Appl., Vol. 28, No. 11, hal. 3239–3258, Nov. 2017.

A. Subasi, A. Alkan, E. Koklukaya, dan M. K. Kiymik, “Wavelet Neural Network Classification of EEG Signals by Using AR Model with MLE Preprocessing,” Neural Netw., Vol. 18, No. 7, hal. 985–997, Sep. 2005.

H. Baali, A. Khorshidtalab, M. Mesbah, dan M.J.E. Salami, “A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification,” IEEE J. Transl. Eng. Health Med., Vol. 3, hal. 1–8, 2015.

S. Theodoridis dan K. Koutroumbas, Pattern Recognition, 4th. ed. Amsterdam, Netherlands: Elsevier Acad. Press, 2009.

F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, dan B. Arnaldi, “A Review of Classification Algorithms for EEG-Based Brain–Computer Interfaces,” J. Neural Eng., Vol. 4, No. 2, hal. R1-R13, 2007.

Q. Zhao, T.M. Rutkowski, L. Zhang, dan A. Cichocki, “Generalized Optimal Spatial Filtering Using a Kernel Approach with Application to EEG Classification,” Cogn. Neurodyn., Vol. 4, No. 4, hal. 355–358, Des. 2010.

S.K. Bashar, A.R. Hassan, dan M.I.H. Bhuiyan, "Identification of Motor Imagery Movements from EEG Signals Using Dual Tree Complex Wavelet Transform," 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015, hal. 290-296.

Z. Tang, C. Li, dan S. Sun, “Single-Trial EEG Classification of Motor Imagery Using Deep Convolutional Neural Networks,” Opt. - Int. J. Light Electron Opt., Vol. 130, hal. 11–18, Feb. 2017.

M. Tangermann, K-R.Müller, A. Aertsen, N. Birbaumer, C. Braun, C. Brunner, R. Leeb, C. Mehring, K. Miller, G. Mueller-Putz, G. Nolte, G. Pfurtscheller, H. Preissl, G. Schalk, A. Schlögl, C. Vidaurre, S. Waldert, dan B. Blankertz, “Review of the BCI Competition IV,” Front. Neurosci., Vol. 6, hal 55-85, 2012.

S. Sanei dan J.A. Chambers, EEG signal processing, Reprinted with corr. Chichester, UK: Wiley, 2009.

L.F. Nicolas-Alonso dan J. Gomez-Gil, “Brain Computer Interfaces, a Review,” Sensors, Vol. 12, No. 12, hal. 1211–1279, Jan. 2012.

Z. Tang, S. Sun, S. Zhang, Y. Chen, C. Li, dan S. Chen, “A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control,” Sensors, Vol. 16, No. 12, hal. 1-14, Des. 2016.

G.K. Verma dan U.S. Tiwary, “Multimodal Fusion Framework: A Multiresolution Approach for Emotion Classification and Recognition from Physiological Signals,” NeuroImage, Vol. 102, hal. 162–172, Nov. 2014.

M.K.M. Rahman dan M.A.M. Joadder, “A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison,” Appl. Theory Comput. Technol., Vol. 2, No. 2, hal. 1, Mar. 2017.

R. Darmakusuma, A.S. Prihatmanto, A. Indayanto, dan T.L. Mengko, “Deteksi Intensi Pergerakan Jari Menggunakan Metode Power Spectral Density dengan Stimuus Visual,” J. Nas. Tek. Elektro dan Teknol. Inf. JNTETI, Vol. 4, No. 2, , hal. 125-129. 2015.

(2014) Brain Science Institute RIKEN Website. “EEG Datasets from BCI Experiment,” [Online] http://www.bsp.brain.riken.jp/~qibin/ homepage/Datasets.html, tanggal akses: 8 Mar. 2016.

(2008) BCI Competition Website. “Data sets 2a”, [Online] http://www.bbci.de/competition/iv/#dataset2a, tanggal akses: 22 Agt. 2017 .

A. Rizal, “Perbandingan Skema Dekomposisi Paket Wavelet untuk Pengenalan Sinyal EKG,” J. Nas. Tek. Elektro Dan Teknol. Inf. JNTETI, Vol. 4, No. 2, hal 80-86, 2015.

Y.T. Putranto, M. Hariadi, T.A. Sardjono, dan M.H. Purnomo, “Enhancement of EEG Signals Classification for Imaginary Movement By Detailing Discriminant Parameters,” 2016 IEEE Region 10 Conference (TENCON), 2016, hal. 47–50.

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




DOI: http://dx.doi.org/10.22146/jnteti.v8i1.493

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