標題: Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
作者: Zao, John K.
Gan, Tchin-Tze
You, Chun-Kai
Chung, Cheng-En
Wang, Yu-Te
Mendez, Sergio Jose Rodriguez
Mullen, Tim
Yu, Chieh
Kothe, Christian
Hsiao, Ching-Teng
Chu, San-Liang
Shieh, Ce-Kuen
Jung, Tzyy-Ping
交大名義發表
資訊工程學系
National Chiao Tung University
Department of Computer Science
關鍵字: brain computer interfaces;bio-sensors;machine-to-machine communication;semantic sensor web;linked data;Fog Computing;Cloud Computing
公開日期: 3-Jun-2014
摘要: EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.
URI: http://dx.doi.org/10.3389/fnhum.2014.00370
http://hdl.handle.net/11536/147756
ISSN: 1662-5161
DOI: 10.3389/fnhum.2014.00370
期刊: FRONTIERS IN HUMAN NEUROSCIENCE
Volume: 8
起始頁: 0
結束頁: 0
Appears in Collections:Articles


Files in This Item:

  1. a1078dbe9cd8f362c0fb1200cfe687b8.pdf