Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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The Brainwave Analysis of Server System Based on Spring Framework

스프링 프레임워크 기반의 뇌파 분석 서버 시스템

  • 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)
  • 최성자 (한남대학교 멀티미디어공학과) ;
  • 김귀정 (백석대학교 정보통신공학부) ;
  • 강병권 (순천향대학교 정보통신공학과)
  • Received : 2018.11.14
  • Accepted : 2019.02.20
  • Published : 2019.02.28
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Abstract

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.

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.

Keywords

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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

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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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