단일 채널에서 블라인드 음원분리를 통한 하이브리드 BCI시스템 최적화
The Optimization of Hybrid BCI Systems based on Blind Source Separation in Single Channel
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본 논문에서는 모션인식을 활용한 Human UI/UX 를 위한 IoT 기반 스마트헬스 서비스를 제안한다 . 현 재까지 M2M 기반의 u-healthcare 에서 적용되는 센서 네트워크에서는 TCP/IP 프로토콜이 아닌 non-IP 프로토콜을 이용하고 있다 . 그러나 서비스의 이용 확대와 IoT 기반의 센서 네트워크 관리를 용이하게 하기 위해서는 다수 의 센서들의 인터넷 연결이 반드시 요구된다 . 따라서 센서들에 의하여 측정된 자료들을 인터넷과 통신하는 것 은 물론 이동이 가능해야 하기 때문에 네트워크 이동성을 고려한 IoT 기반 스마트헬스 서비스를 설계하였다 . 또한 IoT 기반 스마트헬스 서비스는 기존의 헬스케어 플랫폼과는 다르게 바이오 정보뿐만이 아니라 동작감지를 위한 스마트 헬스 서비스를 개발하였다 . u-healthcare 에서 사용되는 WBAN 통신은 일반적으로 많은 네트워크화 된 장치 및 게이트웨이로 구성된다 . 본 논문에서 제안하는 방법은 WBAN 센서 노드간의 이동성을 지원하는 기 술을 이용함으로써 무선 환경에서도 동적 변화에 쉽게 대응할 수 있고 , 사용자의 동작감지를 통해 체계적인 관 리가 이루어진다
Abstract
In this paper, we proposed IoT based Smart Health Service using Motion Recognition for Human UX/UI. Until now, sensor networks using M2M-based u-healthcare are using non-IP protocol instead of TCP / IP protocol. However, in order to increase the service utilization and facilitate the management of the IoT-based sensor network, many sensors are required to be connected to the Internet. Therefore, IoT-based smart health service is designed considering network mobility because it is necessary to communicate not only the data measured by sensors but also the Internet. In addition, IoT-based smart health service developed smart health service for motion detection as well as bio information unlike existing healthcare platform. WBAN communications used in u-healthcare typically consist of many networked devices and gateways. The method proposed in this paper can easily cope with dynamic changes even in a wireless environment by using a technology supporting mobility between WBAN sensor nodes, and systematic management is performed through detection of a user's motion
참고문헌 (18)
- T. Ebrahimi, "Brain-computer interface in multimedia communication", IEEE Signal Processing Magazine, 20, pp.14~24 (2003)
- Nicolas-Alonso, "Brain computer interfaces, a review", Sensor, 12(2), pp.1211~1279 (2012)[DOI]
- L.-W. Ko, "Combining CCA and CFP for enhancing the performance in the hybrid BCI system", 2015 IEEE Symposium Series on Computational Intelligence, pp.103~108 (2015)
- L.-W. Ko, "Developing a few-channel hybrid BCI system by using motor imagery with SSVEP assist", 2014 International JointConference on Neural Networks (IJCNN), pp.4114~4120 (2014)
- J. R. Wolpaw, "Brain-computer interfaces for communication and control", Clinical Neurophysiology, 113, pp.767~791 (2002)[DOI]
- [URL]
- A. Hyvarinen, "Survey on independent component analysis", Neural Computing Surveys, 2, pp.94~128 (1999)
- C.W.N.F. Che Wan Fadzal, "An Analysis of EEG Signal Generated From Grasping and Writing", Proceedings of 2011 IEEE Conference on Computer Application & Industrial Electronics (ICCAIE 2011) (2011), IEEE
- Muller-Gerking J, "Designing optimal spatial filters for single-trial EEG classification in a movement task", Clin. Neurophysiol., 110, pp.787~798 (1999)[DOI]
- Hyvarinen, A, "Fast and robust fixed-point algorithms for independent component analysis", IEEE T. Neural Netw., 10, pp.626~634 (1999)[DOI]
- Sanjeev, N.J., "Blind source separation and ICA techniques: A review", Int. J. Eng. Sci. Technol., 4(04), pp.1490~1503 (2012)
- Olyaee, S., "A comparative study of two blind source separation approaches to resolve the multisource limitation of the nutating rising-sun reticle based optical trackers", Int. J. Comput. Electr. Eng., 2(2), pp.1793~8163 (2010)
- Gao, J., "Automatic removal of eye-movement and blink artifacts from EEG signals", Brain Topogr., 23, pp.105~114 (2010)[DOI]
- Nicolas-Alonso, L.F., "Brain Computer Interfaces, a review", Sensors, 12, pp.1211~1279 (2012)[DOI]
- L.-W. Ko, "Developing a few-channel hybrid BCI system by using motor imagery with SSVEP assist", 2014 International Joint Conference on Neural Networks (IJCNN), pp.4114~4120 (2014)
- C. Brunner, "Improved signal processing approaches in an online simulation of a hybrid brain-computer interface", J. Neurosci. Methods, 188(1), pp.165~173 (2010)[DOI]
- Yang D, "An Online Synchronous Hybrid BCI System for Multidimensional Control Using MI and SSVEP", Summer Conference of IEIE, pp.904~907 (2017)
- Jiang Y, "A Hybrid Brain-Computer Interface System for Multidimensional Control Using Motor Imagery and Eye Closure", Journal of Medical Imaging and Health Informatics, 7(7), pp.1580~1588 (2017)[DOI]