Browse > Article
http://dx.doi.org/10.9708/jksci.2020.25.08.111

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)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.8, 2020 , pp. 111-117 More about this Journal
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(BCI);
Citations & Related Records
연도 인용수 순위
  • Reference
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   DOI
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.   DOI
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   DOI
4 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.   DOI
5 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   DOI
6 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   DOI
7 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.   DOI
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.   DOI
11 Chollet, F. Keras: The python deep learning library. Astrophys. Source Code Libr. 2018. https://keras.io/k
12 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.   DOI
13 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.
14 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.   DOI
15 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.   DOI
16 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   DOI
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