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BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD

    Authors

    • Dr. Vikas Jain 1
    • Dr.S. Kirubakaran 2
    • Dr.G. Nalinipriya 3
    • Binny. S 4
    • Dr.M. Maragatharajan 5

    1 Assistant Professor, Department of Computer Application, Sir Chhotu Ram Institute of Engineering and Technology, Ch. Charan Singh University Meerut, Uttar Pradesh, India- 250004.

    2 Professor, Department of Computer Science and Engineering , Jayamukhi Institute of Technological Sciences,Chennaraopet Mandal,Narsampet, Nekkonda Rd,Arshanapally, Telangana 506332.

    3 Professor, Department of Information Technology, Saveetha Engineering College, Saveetha Nagar, Chennai, Tamil Nadu 602105

    4 Associate Professor, Department of Master of Computer Applications, Kristu Jyoti College of Management and Technology, Changanacherry, Kottayam, 686104

    5 Assistant Professor, Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu 626126.

,

Document Type : Research Article

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Abstract

Brain-computer interface (BCI) decoding connects the human nervous world to the external world.
People's brain signals to commands that computer devices can detect. In-depth study the performance
of brain-computer interface systems has recently increased. In this article, we will systematically
Investigate brain signal types for BCI and explore relevant in-depth study concepts for brain signal
analysis. In this study, we have a comparison of different traditional classification algorithms new
methods of in-depth study. We explore two different types Deep learning methods, i.e., traditional
neural networks Architecture with Long Short term Memory and Repetitive Neural Networks. We
check the classification Accuracy of Recent 5-Class Study-State Visual Evoked Opportunities dataset.
The results demonstrate in-depth expertise learning methods compared to traditional taxonomy
Algorithms.

Keywords

  • Deep learning
  • Brain Computer Interface
  • neural network
  • Taxonomy Algorithm
  • Classification
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    • Article View: 258
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European Journal of Molecular & Clinical Medicine
Volume 7, Issue 2
November 2020
Page 3294-3301
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  • Article View: 258
  • PDF Download: 426

APA

Jain, D. V., Kirubakaran, D., Nalinipriya, D., S, B., & Maragatharajan, D. (2020). BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD. European Journal of Molecular & Clinical Medicine, 7(2), 3294-3301.

MLA

Dr. Vikas Jain; Dr.S. Kirubakaran; Dr.G. Nalinipriya; Binny. S; Dr.M. Maragatharajan. "BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD". European Journal of Molecular & Clinical Medicine, 7, 2, 2020, 3294-3301.

HARVARD

Jain, D. V., Kirubakaran, D., Nalinipriya, D., S, B., Maragatharajan, D. (2020). 'BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD', European Journal of Molecular & Clinical Medicine, 7(2), pp. 3294-3301.

VANCOUVER

Jain, D. V., Kirubakaran, D., Nalinipriya, D., S, B., Maragatharajan, D. BRAIN COMPUTER INTERFACE SYSTEM USING DEEP CONVOLUTIONAL MACHINE LEARNING METHOD. European Journal of Molecular & Clinical Medicine, 2020; 7(2): 3294-3301.

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