Implementation of Educational Brain Motion Controller for Machine Learning Applications (머신러닝 학습을 위한 교육용 모션 컨트롤러의 구현)

Myeong-Chul Park (박명철); Choi Duk Kyu (최덕규); Tae-Sun Kim (김태선)

doi:10.9708/jksci.2020.25.08.111

Implementation of Educational Brain Motion Controller for Machine Learning Applications

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(8), pp.111-117
DOI : 10.9708/jksci.2020.25.08.111
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 28, 2020
Accepted : August 24, 2020
Published : August 31, 2020

Myeong-Chul Park ¹, Choi Duk Kyu ¹, Tae-Sun Kim ¹

¹경운대학교

Accredited

ABSTRACT

Recently, with the high interest of machine learning, the need for educational controllers to interface with physical devices has increased. However, existing controllers are limited in terms of high cost and area of utilization for educational purposes. In this paper, motion control controllers using brain waves are proposed for the purpose of students' machine learning applications. The brain motion that occurs when imagining a specific action is measured and sampled, then the sample values were learned through Tensor Flow and the motion was recognized in contents such as games. Movement variation for motion recognition consists of directionality and jump motion. The identification of the recognition behavior is sent to a game produced by an Unreal Engine to operate the character in the game. In addition to brain waves, the implemented controller can be used in various fields depending on the input signal and can be used for educational purposes such as machine learning applications.

KEYWORDS

Machine Learning, Brainwave, Tensor Flow, Brain Computer Interface

Citation status

* References for papers published after 2023 are currently being built.

[journal] D. Puthankattil Subha / 2010 / EEG Signal Analysis: A Survey / Journal of Medical Systems / Springer Science and Business Media LLC 34(2) : 195~212

[journal] Korovesis, N. / 2019 / Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves / Electronics / MDPI AG 8(12) : 1387~

[journal] U. Rajendra Acharya / 2018 / Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals / Computers in Biology and Medicine / Elsevier BV 100 : 270~278

[journal] Tabar YR / 2017 / A novel deep learning approach for classification of EEG motor imagery signals / J Neural Eng 14(1)

[journal] No-Sang Kwak / 2017 / A convolutional neural network for steady state visual evoked potential classification under ambulatory environment / PLOS ONE / Public Library of Science (PLoS) 12(2) : e0172578~

[journal] H Cecotti / 2011 / Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 33(3) : 433~445

[journal] Jozsef Katona / 2016 / A Brain–Computer Interface Project Applied in Computer Engineering / IEEE Transactions on Education / Institute of Electrical and Electronics Engineers (IEEE) 59(4) : 319~326

[journal] Katona, Jozsef / 2018 / The Evaluation of BCI and PEBL-based Attention Tests / Acta Polytechnica Hungarica / Obuda University 15(3)

[journal] Katona, Jozsef / 2018 / Examining the Learning Efficiency by a Brain-Computer Interface System / Acta Polytechnica Hungarica / Obuda University 15(3)

[journal] Sarah N. Abdulkader / 2015 / Brain computer interfacing: Applications and challenges / Egyptian Informatics Journal / Elsevier BV 16(2) : 213~230

[journal] Luis Fernando Nicolas-Alonso / 2012 / Brain Computer Interfaces, a Review / Sensors / MDPI AG 12(2) : 1211~1279

[confproc] F. Amato / 2017 / Multilayer Perceptron: An Intelligent Model for Classification and Intrusion Detection / 31st International Conference on Advanced Information Networking and Applications Workshops : 686~691

[journal] Gopal Chandra Jana / 2018 / Enhancing the performance of motor imagery classification to design a robust brain computer interface using feed forward back-propagation neural network / Ain Shams Engineering Journal / Elsevier BV 9(4) : 2871~2878

[journal] Pieter-Tjerk de Boer / 2005 / A Tutorial on the Cross-Entropy Method / Annals of Operations Research / Springer Science and Business Media LLC 134(1) : 19~67

[journal] Pauli Virtanen / 2020 / SciPy 1.0: fundamental algorithms for scientific computing in Python / Nature Methods / Springer Science and Business Media LLC 17(3) : 261~272

[web] Chollet, F. / 2018 / Keras: The python deep learning library. Astrophys. Source Code Libr / https://keras.io/k

[confproc] Abadi, M. / 2016 / TensorFlow : A system for large-scale machine learning / Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation : 265~283

[web] / EEG Deep Learning Library / https://github.com/SuperBruceJia/EEG-DL

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Implementation of Educational Brain Motion Controller for Machine Learning Applications

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Implementation of Educational Brain Motion Controller for Machine Learning Applications

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.