Klasifikasi Pergerakan Tangan dan Kaki Berbasis Sinyal EEG Menggunakan Common Spatial Patterns dan Multilayer Perceptron Backpropagation

Rahmat Widadi, Dodi Zulherman


The artificial bionic appendicular systems, such as hands and feet movement, require the Brain-Computer Interface (BCI) to control the movement. The BCI based movement controlling in a bionic device utilises the EEG signal directly, but the EEG signal classification has to design before it implemented in a BCI device. This study aims to design a classification system for hands and feet movements based on beta channel EEG signals. The system design used the Common Spatial Pattern (CSP) method for feature extraction and stochastic gradient descent in Multilayer Perceptron Backpropagation (MLP-BP) for the hands and feet movement classification. We use the EEG signal from ten subjects to evaluate the design. Also, the variation number of node in MLP-BP to get the system performance based on the confusion matrix. Based on the test results, the improvement of the number of nodes brought the accuracy increasing, especially for variation two, four and eight nodes. The highest mean of system design accuracy reached 94.38% for eight nodes.

Teknologi brain computer interface (BCI) dibutuhkan untuk mekanisme pengaturan gerak dari alat bantu bionik sistem appendicular, khususnya tangan dan kaki. Pengendali pergerakan devais bionik berbasis BCI dapat menggunakan sinyal EEG, namun sistem pengelompokan sinyal EEG untuk pergerakan tangan dan kaki dibutuhkan sebagai tahapan awal pengimplementasian tersebut. Penelitian ini bertujuan untuk merancang sebuah sistem klasifikasi pergerakan tangan dan kaki berdasarkan sinyal EEG gelombang beta. Rancangan sistem menggunakan metode Common Spatial Patterns (CSP) pada tahap ekstrasi fitur dan stochastic gradient descent dalam Multilayer Perceptron Backpropagation (MLP-BP) untuk
mengklasifikasi pergerakan tangan dan kaki. Pengujian sistem menggunakan confusion matrix terhadap sepuluh subjek dan variasi jumlah node MLP-BP. Berdasarkan hasil pengujian, nilai rata-rata akurasi sistem dengan jumlah node dua, empat dan delapan meningkat sebanding dengan peningkatan jumlah node, namun nilai rata-rata akurasi menurun kembali saat jumlah node ditingkatkan menjadi enam belas. Rata-rata akurasi rancangan sistem yang tertinggi mencapai 94,38 % pada sistem dengan delapan node pada hidden layer.


BCI, Hands and Feet, EEG, CSP, MLP-BP

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J. N. Mak and J. R. Wolpaw, “Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects,” IEEE Rev. Biomed. Eng., vol. 2, pp. 187–199, 2009.

D. B. MacDonald, “Electroencephalography: Basic Principles and Applications,” in International Encyclopedia of the Social & Behavioral Sciences (Second Edition), Second Edi., J. D. Wright, Ed. Oxford: Elsevier, 2015, pp. 353–363.

H. Ramoser, J. Muller-Gerking, and G. Pfurtscheller, “Optimal spatial filtering of single trial EEG during imagined hand movement,” IEEE Trans. Rehabil. Eng., vol. 8, no. 4, pp. 441–446, 2000.

G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, and K.-. Muller, “Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing,” IEEE Trans. Biomed. Eng., vol. 53, no. 11, pp. 2274–2281, 2006.

B. Reuderink and M. Poel, Robustness of the Common Spatial Patterns algorithm in the BCI-pipeline, no. DTR08-9/TR-CTIT-08–52. Netherlands: Centre for Telematics and Information Technology (CTIT), 2008.

S. Selim, M. M. Tantawi, H. A. Shedeed, and A. Badr, “A CSPAM-BA-SVM Approach for Motor Imagery BCI System,” IEEE Access, vol. 6, pp. 49192–49208, 2018.

M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, and A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” in 2018 26th European Signal Processing Conference (EUSIPCO), 2018, pp. 1690–1694.

F. Lotte and C. Guan, “Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms,” IEEE Trans. Biomed. Eng., vol. 58, no. 2, pp. 355–362, 2011.

Y. Kim, J. Ryu, K. K. Kim, C. C. Took, D. P. Mandic, and C. Park, “Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns,” Comput. Intell. Neurosci., vol. 2016, pp. 1–13, 2016.

Z. Tang, C. Li, J. Wu, P. Liu, and S. Cheng, “Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI,” Front. Inf. Technol. Electron. Eng., vol. 20, no. 8, pp. 1087–1098, 2019.

S. B. Wankhede, “Analytical Study of Neural Network Techniques: SOM, MLP and Classifier-A Survey,” IOSR J. Comput. Eng., vol. 16, no. 3, pp. 86–92, 2014.

G. Panchal, A. Ganatra, Y. P. Kosta, and D. Panchal, “Behaviour Analysis of Multilayer perceptronswith Multiple Hidden Neurons and Hidden Layers,” Int. J. Comput. Theory Eng., vol. 3, no. 2, pp. 332–337, 2011.

A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet,” Circulation, vol. 101, no. 23, pp. e215–e220, 2000.

G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, “BCI2000: a general-purpose brain-computer interface (BCI) system,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1034–1043, 2004.

B. Mulgrew, P. Grant, and J. Thompson, “Finite impulse response digital filters,” in Digital Signal Processing: Concepts and Applications, B. Mulgrew, P. Grant, and J. Thompson, Eds. London: Macmillan Education UK, 1999, pp. 150–175.

A. Widmann, E. Schröger, and B. Maess, “Digital filter design for electrophysiological data – a practical approach,” J. Neurosci. Methods, vol. 250, pp. 34–46, 2015.

Z. J. Koles, M. S. Lazar, and S. Z. Zhou, “Spatial patterns underlying population differences in the background EEG,” Brain Topogr., vol. 2, no. 4, pp. 275–284, Jun. 1990.

R. Widadi, I. Soesanti, and O. Wahyunggoro, “EEG Classification Using Elliptic Filter and Multilayer perceptron Based on Gamma Activity Features,” in Proceedings - 2018 4th International Conference on Science and Technology, ICST 2018, 2018.

G. Ellis, “Chapter 9 - Filters in Control Systems,” in Control System Design Guide (Fourth Edition), G. B. T.-C. S. D. G. (Fourth E. Ellis, Ed. Boston: Butterworth-Heinemann, 2012, pp. 165–183.

P. Virtanen et al., “SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python,” arXiv e-prints, p. arXiv:1907.10121, Jul. 2019.

A. Gramfort et al., “MEG and EEG data analysis with MNE-Python,” Front. Neurosci., vol. 7, p. 267, 2013.

F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011


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