Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Implementation of Brain-machine Interface System using Cloud IoT

클라우드 IoT를 이용한 뇌-기계 인터페이스 시스템 구현

  • Hoon-Hee Kim (Dept. of Computer Engineering, Pukyong National University)
  • 김훈희 (부경대학교 컴퓨터공학부)
  • Received : 2022.12.27
  • Accepted : 2023.02.06
  • Published : 2023.02.28
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Abstract

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

뇌-기계 인터페이스는 차세대 인터페이스로서 기기 이용자가 명령을 생각할 때 발생하는 신경세포의 전기적 신호인 뇌파를 해석하여 기기를 조종하는 인터페이스다. 뇌-기계 인터페이스는 다양한 스마트기기 등에 응용될 수 있지만 뇌파 신호를 해석하는 데는 상당량의 계산 프로세스가 필요하다. 따라서 에지(Edge) 형태로 구현된 임베디드 시스템에서는 뇌-기계 인터페이스를 구현하기가 어렵다. 본 연구에서는 사물인터넷 기술을 이용하여 에지에서는 뇌파 측정만을 진행하고 뇌파 데이터의 저장 및 분석은 클라우드 컴퓨팅에서 수행하는 새로운 형태의 뇌-기계 인터페이스 시스템을 제안하였다. 본 시스템은 뇌-기계 인터페이스를 위한 정량 뇌파 분석을 성공적으로 수행하였으며 데이터 송수신 시간 또한 실시간 처리가 가능한 수준을 보였다.

Keywords

Acknowledgement

본 논문은 과학치안 공공연구성과 실용화 촉진 시범사업 연구과제(No. 1711174175)로 수행되었음.

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