Klasifikasi Keadaan Mata Berdasarkan sinyal EEG menggunakan Extreme Learning Machines
Abstract views: 628
,
PDF downloads: 1655
Abstract
penelitian saat ini. Sinyal EEG memiliki banyak manfaat seperti pendeteksian epilepsi, gangguan
tidur, atau input dalam aplikasi komputer. Salah satu input yang dapat dideteksi berdasarkan sinyal
EEG adalah keadaan mata. Namun untuk digunakan sebagai input dalam aplikasi diperlukan
klasifikasi dengan performansi yang memadai. Oleh karena itu penulis akan dilakukan penelitian
dimana salah satu metode pembelajaran Jaringan Syaraf Tiruan yaitu Extreme Learning Machine
(ELM) akan diimplementasikan untuk mengklasifikasikan kondisi mata berdasarkan sinyal EEG.
Dataset yang digunakan untuk melatih dan menguji model adalah dataset eye-state yang
didonasikan oleh Oliver Roesler digabung dengan dataset yang berasal dari website repository
Universitas of California, IrvineI (UCI) . Terdapat 7 corpus yang terdiri dari perekaman EEG
yang dilakukan kepada 4 orang berbeda, lalu ditambahkan 1 corpus yang merupakan
penggabungan seluruh corpus lain. Dari hasil pengujian yang dilakukan disimpulkan bahwa ELM
dapat digunakan untuk klasifikasi keadaan mata dengan akurasi mencapai 97,95% dengan waktu
latih hanya 0,81 detik jika masing-masing data digunakan secara terpisah, sedangkan
penggabungan keseluruhan dataset hanya mencapai akurasi 78,94% dengan waktu latih 5,71 detik.
Downloads
References
Chen, L., L. Li, and L. Xiao. "Differences of EEG between Eyes-Open and Eyes-Closed States Based on Autoregressive Method."
Journal Of Electronic Science And Technology Of China, 2009: 175-179.
Roesler, Oliver, dan David Suendermann. “A First Step towards Eye State Prediction Using EEG.†in Proceedings of International
Conference on Applied Informatics for Health and Life Science. Istanbul: Turkey, 2013.
Ersa Christian Prakoso et.al.
Klasifikasi Keadaan Mata Berdasarkan Sinyal... 114
Huang, Guang-Bin, Qin-Yu Zhu, dan Chee-Kheong Siew. “Extreme Learning Machine: Theory and applications.â€
Neurocomputing 70, 2006: 489–501.
Zheng, Wei-Long, Jia-Yi Zhu, Yong Peng, dan Bao-Liang Lu. “EEG-based emotion classification using deep belief networks.â€
IEEE International Conference on Multimedia and Expo. Chengdu: IEEE, 2014. 1-6.
Naibaho, Vania Cathlin Raissa. Klasifikasi Emosi Melalui Sinyal EEG yang Dihasilkan Otak dengan Menggunakan Discrete
Wavelet Transform dan Backpropagation Artificial Neural Network. Undergraduate Thesis, Bandung: Telkom University, 2013.
Suyanto. Artificial Intelligence. Bandung: Informatika Bandung, 2009.
Huang, Guang-Bin, Hongming Zhou, Xiaojian Ding, dan Rui Zhang. “Extreme Learning Machine for Regression and Multiclass
Classification.†IEEE Transaction on Systems, Man, and Cybernetics, 2012: 513-529.
Tang, Jiexiong, Chenwei Deng, dan Guang-Bin Huang. “Extreme Learning Machine for Multilayer Perceptron.†IEEE
Transactions on Neural Networks And Learning Systems, 2013: 809 - 821.
Roesler, Oliver, Lucas Bader, Jan Forster, Yoshikatsu Hayashi, Stefan Hebler, dan David Suendermann. “Comparison of EEG
Devices for Eye State Classification.†International Conference on Applied Informatics for Health and Life Sciences (AIHLS),
Kusadasi, Turkey, 2014.
Wang, Ting, Sheng-Uei Guan, Ka Lok Man, dan T. O. Ting. “Time Series Classifiaction for EEG Eye State Identification based on
Incremental Attribute Learning.†International Symposium on Computer, Consumer and Control. Taichung: 2014, 2014. 158 -
Zhu, Jia-yi, Wei-Long Zheng, Yong Peng, Ruo-Nan Duan, dan Bao-Liang Lu. “EEG-based Emotion Recognition using
Discriminative Graph Regularized Extreme Learning Machine.†International Joint Conference on Neural Networks, 2014: 525-
Peng, Yong, Jia-Yi Zhu, Wei-Long Zheng, dan Bao-Liang Lu. “EEG-based Emotion Recognition with Manifold Regularized
Extreme Learning Machine.†36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,
: 974-977.
Huang, Guang-Bin. “What are Extreme Learning Machines? Filling the Gap Between between Frank Rosenblatt's Dream and John
von Neumann's Puzzle.†Cognitive Computation 7, 2015: 263-278.
Huang, Gao, Shiji Song, Keyou You, dan Guang-Bin Huang. “Trends in extreme learning machines: A review.†Neural Networks,
: 32-48.
Copyright (c) 2016 Ersa Christian Prakoso
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Manuscript submitted to IndoJC has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals.Â
- Copyright on any article is retained by the author(s). Regarding copyright transfers please see below.
- Authors grant IndoJC a license to publish the article and identify itself as the original publisher.
- Authors grant IndoJC commercial rights to produce hardcopy volumes of the journal for sale to libraries and individuals.
- Authors grant any third party the right to use the article freely as long as its original authors and citation details are identified.
- The article and any associated published material is distributed under the Creative Commons Attribution 4.0License
