한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제25권8호
  • /
  • Pages.111-117
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  • 2020
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Implementation of Educational Brain Motion Controller for Machine Learning Applications

  • Park, Myeong-Chul (Dept. of Avionics Engineering, Kyungwoon University) ;
  • Choi, Duk-Kyu (Dept. of Avionics Engineering, Kyungwoon University) ;
  • Kim, Tae-Sun (Dept. of Avionics Engineering, Kyungwoon University)
  • 투고 : 2020.07.28
  • 심사 : 2020.08.24
  • 발행 : 2020.08.31
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초록

최근 머신러닝의 높은 관심과 더불어 물리적 장치에 연동하기 위한 교육용 컨트롤러의 필요성이 증대되고 있다. 하지만 기존 컨트롤러는 교육용으로서의 고비용과 활용 영역면에서 제한적이다. 본 논문에서는 학생들의 머신 러닝 학습을 목적으로 뇌파를 이용한 동작 제어 컨트롤러를 제안한다. 특정 행위를 상상할 때 발생하는 뇌의 동작 상상 뇌파를 측정하여 표본화 한 후, Tensor Flow를 통하여 표본값을 학습시키고 게임 등의 콘텐츠에서 동작을 인식할 수 있도록 설계하였다. 동작 인식을 위한 움직임 변이는 상하좌우의 방향성과 점프 동작으로 구성된다. 인식 동작의 식별 정보를 언리얼 엔진으로 제작한 게임에 전송하여 게임 속 캐릭터를 동작시키는 절차로 이루어 진다. 구현된 컨트롤러는 뇌파 외에도 입력 신호에 따라 다양한 분야에 활용될 수 있으며 머신 러닝 학습 등의 교육적 용도로 사용될 수 있을 것이다.

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.

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참고문헌

  1. Subha, D.P.; Joseph, P.K.; Acharya, R.; Lim, C.M. "EEG signal analysis: A survey," J. Med. Syst., Vol. 34, pp. 195-212, April 2010. DOI: 10.1007/s10916-008-9231-z
  2. Korovesis, N.; Kandris, D.; Koulouras, G.; Alexandridis, A. "Robot Motion Control via an EEG-Based Brain-Computer Interface by Using Neural Networks and Alpha Brainwaves," Electronics, Vol.8(12), 1387, Nov. 2019. DOI: 10.3390/electronics8121387.
  3. U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals," Computers in Biology and Medicine, Vol. 100(1), pp. 270-278, Sep. 2018. DOI: 10.1016/j.compbiomed.2017.09.017
  4. Tabar YR and Halici U. "A novel deep learning approach for classification of EEG motor imagery signals," J Neural Eng. Vol. 14(1):016003, Nov. 2017. DOI: 10.1088/1741-2560/14/1/016003
  5. Kwak NS, Muller KR, Lee SW, "A convolutional neural network for steady state visual evoked potential classification under ambulatory environment," PLOS ONE, Vol. 12(2): e0172578, Feb. 2017. DOI: 10.1371/journal.pone.0172578
  6. H. Cecotti and A. Graser, "Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces," in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 3, pp. 433-445, March 2011. DOI:10.1109/TPAMI.2010.125.
  7. J. Katona and A. Kovari, "A Brain-Computer Interface Project Applied in Computer Engineering," in IEEE Transactions on Education, Vol. 59, No. 4, pp. 319-326, Nov. 2016. DOI:10.1109/TE.2016.2558163.
  8. Katona, Jozsef and Kovarii, Attila, "The evaluation of BCI and PEBL-based attention tests," Acta Polytechnica Hungarica, Vol.15, No. 1, June 2018. DOI: 10.12700/APH.15.3.2018.3.13.
  9. Katona, Jozsef and Kovarii, Attila, "Examining the learning efficiency by a brain-computer interface system," Acta Polytechnica Hungarica Vol. 15, No. 1, June 2018. DOI:10.12700/APH.15.3.2018.3.14.
  10. Sarah N. Abdulkader, Ayman Atia, Mostafa-Sami M. Mostafa, "Brain computer interfacing: Applications and challenges," Egyptian Informatics Journal, Vol. 16(2), pp. 213-230, July 2015. DOI: 10.1016/j.eij.2015.06.002.
  11. Nicolas-Alonso, Luis Fernando, and Jaime Gomez-Gil. "Brain computer interfaces, a review," Sensors, Vol. 12(2), pp. 1211-1279, Jan. 2012. DOI: 10.3390/s120201211.
  12. F. Amato, N. Mazzocca, F. Moscato and E. Vivenzio, "Multilayer Perceptron: An Intelligent Model for Classification and Intrusion Detection," 31st International Conference on Advanced Information Networking and Applications Workshops, pp. 686-691, May 2017. DOI: 10.1109/WAINA.2017.134.
  13. Jana, G.C.; Swetapadma, A.; Pattnaik, P.K. "Enhancing the performance of motor imagery classification to design a robust brain computer interface using feed forward back-propagation neural network," Ain Shams Eng. J. Vol. 9(4), pp. 2871-2878, Dec. 2018. DOI: 10.1016/j.asej.2017.12.003.
  14. De Boer, P., Kroese, D.P., Mannor, S. et al. "A Tutorial on the Cross-Entropy Method," Ann Oper Res, Vol. 134, pp. 19-67, Feb. 2005. DOI: 10.1007/s10479-005-5724-z.
  15. Virtanen, P., Gommers, R., Oliphant, T.E. et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python," Nature Methods, Vol. 17, pp. 261-272, Feb. 2020. DOI:10.1038/s41592-019-0686-2
  16. Chollet, F. Keras: The python deep learning library. Astrophys. Source Code Libr. 2018. https://keras.io/k
  17. Abadi, M.; Barham, P.; Chen, J. et al. "TensorFlow: A system for large-scale machine learning," In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, pp.265-283, Nov. 2016.
  18. EEG Deep Learning Library, https://github.com/SuperBruceJia/EEG-DL