디지털융복합연구 (Journal of Digital Convergence)

  • 제17권2호
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  • Pages.155-161
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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스프링 프레임워크 기반의 뇌파 분석 서버 시스템

The Brainwave Analysis of Server System Based on Spring Framework

  • 최성자 (한남대학교 멀티미디어공학과) ;
  • 김귀정 (백석대학교 정보통신공학부) ;
  • 강병권 (순천향대학교 정보통신공학과)
  • Choi, Sung-Ja (Dept. of Multimeida Engineering, Hannam University) ;
  • Kim, Gui-Jung (Division of Information & Communication, Baeseok University) ;
  • Kang, Byeong-Gwon (Dept. of Information and Communication Engineering, Soonchunhyang University)
  • 투고 : 2018.11.14
  • 심사 : 2019.02.20
  • 발행 : 2019.02.28
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⟨ 이전 논문 다음 논문 ⟩

초록

뇌파는 두뇌 활동의 변화를 시간적, 공간적으로 파악할 수 있는 대표적인 수단으로써 인간의 두피에서 측정 가능한 자발적 전기활동이다. 뇌파 전기활동을 제어하기 위해 다양한 인터페이스 기술들이 제공되고 있으며, 뇌파를 통한 휠체어나 로봇과 같은 기계의 조작이 가능하다. 뇌파 데이터의 특성은 실시간으로 다양한 채널 유형으로 수집되며, 이를 분석하기 위한 서버시스템은 플랫폼에 대해 독립적이고 경량화 된 시스템이 요구된다. 스프링 플랫폼은 독립적이고 경량화 된 서버시스템으로서, 엔터프라이즈급의 서버 프레임워크로 비즈니스 영역에서 활용되고 있다. 본 논문에서는 독립적이고 경량화 된 스프링 서버시스템을 활용한 뇌파 분석 시스템을 제안한다. 제안된 시스템을 활용하여 뇌파제어의 신뢰성을 높이고, 분석 및 제어 인터페이스 확장이 가능하다. 또한 게임과 의료용 등 다양한 방면으로도 활용이 가능하다.

Electroencephalography (EEG), a representative method of identifying temporal and spatial changes in brain activity, is a voluntary electrical activity measurable in the human scalp. Various interface technologies have been provided to control EEG activity, and it is possible to operate a machine such as a wheelchair or a robot through brainwaves. The characteristics of EEG data are collected in various types of channels in real time, and a server system for analyzing them is required to have an independent and lightweight system for the platform. In these days, the Spring platform is used as a large business server as an independent, lightweight server system. In this paper, we propose an EEG analysis system using the Spring server system. Using the proposed system, the reliability of EEG control can be enhanced, and analysis and control interface expansion can be provided in various aspects such as game and medical areas.

키워드

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Fig. 1. BCI System

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Fig. 2. Maven build structure

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Fig. 3. Build life cycle

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Fig. 4. Server platform of brainwave analyzer

DJTJBT_2019_v17n2_155_f0005.png 이미지

Fig. 5. OOP Diagram of brainwave execution

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Fig. 6. Log data of server

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Fig. 7. Realtime brainwave console data

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Fig. 8. Startup display

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Fig. 9. Brainwave AnalyserV1.0 execution results

Table 1. Frequency bands of brainwave

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Table 2. Brain waveforms to physical conditions

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Table 3. Repository of maven build

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Table 4. Dependency library setting

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참고문헌

  1. G.. Schalk & E. C. Leuthardt. (2011). Brain-computer interfaces using electrocorticographic signals. IEEE reviews in biomedical engineering, 4, 140-154. https://doi.org/10.1109/RBME.2011.2172408
  2. L. Bi, X. A. Fan & Y. Liu. (2013). EEG-based brain-controlled mobile robots: a survey. IEEE transactions on human-machine systems, 43(2), 161-176. https://doi.org/10.1109/TSMCC.2012.2219046
  3. J. R. Wolpaw, et al. (2000). Brain-computer interface technology: a review of the first international meeting. IEEE transactions on rehabilitation engineering, 8(2), 164-173. https://doi.org/10.1109/TRE.2000.847807
  4. A. N. Malik, J. Iqbal & M. I. Tiwana. (2016). EEG signals classification and determination of optimal feature-classifier combination for predicting the movement intent of lower limb. In Robotics and Artificial Intelligence (ICRAI), 2016 2nd International Conference on (pp. 45-49). IEEE.
  5. X. Gao, D. Xu, M. Cheng & S. Gao. (2003). A BCI-based environmental controller for the motion-disabled. IEEE Transactions on neural systems and rehabilitation engineering, 11(2), 137-140. https://doi.org/10.1109/TNSRE.2003.814449
  6. F. Cincotti et al. (2008). Non-invasive brain-computer interface system: towards its application as assistive technology. Brain research bulletin, 75(6), 796-803. https://doi.org/10.1016/j.brainresbull.2008.01.007
  7. B. Z. Allison et al. (2010). Toward a hybrid brain-computer interface based on imagined movement and visual attention. Journal of neural engineering, 7(2), 026007. https://doi.org/10.1088/1741-2560/7/2/026007
  8. B. S. Zainuddin, Z. Hussain & I. S. Isa. (2014). Alpha and beta EEG brainwave signal classification technique: A conceptual study. In Signal Processing & its Applications (CSPA), 2014 IEEE 10th International Colloquium on (pp. 233-237). IEEE.
  9. D. Wang et al. (2005, May). Measurement and analysis of electroencephalogram (EEG) using directional visual stimuli for brain computer interface. In Active Media Technology, 2005.(AMT 2005). Proceedings of the 2005 International Conference on (pp. 34-39). IEEE.
  10. B. Ulker et al. (2017, June). Relations of attention and meditation level with learning in engineering education. In Electronics, Computers and Artificial Intelligence (ECAI), 2017 9th International Conference on (pp. 1-4). IEEE.
  11. SPRING: https://www.spring.io
  12. MAVEN: https://maven.apache.org
  13. MARIADB: https://www.mariadb.com
  14. S. J. Choi & B. G. Kang. (2014). Prototype design and implementation of an automatic control system based on a BCI. Wireless personal communications, 79(4), 2551-2563. https://doi.org/10.1007/s11277-014-1861-5
  15. NEUROSKY: https://www.neurosky.com