Browsing by Author "Gan, Tchin-Tze"

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Augmenting VR/AR Applications with EEG/EOG Monitoring and Oculo-Vestibular Recoupling
(2016-01-01)
Zao, John K.
;
Jung, Tzyy-Ping
;
Chang, Hung-Ming
;
Gan, Tchin-Tze
;
Wang, Yu-Te
Head-mounted virtual reality and augmented reality displays (a. k. a. VR/AR goggles) created a revolutionary multimedia genre that is seeking ever-broadening applications and novel natural human interfaces. Adding neuromonitoring and neurofeedback to this genre is expected to introduce a new dimension to user interaction with the cyber-world. This study presents the development of a Neuromonitoring VR/AR Goggle armed with electroencephalo- gram and electrooculogram sensors, programmable milli-Ampere current stimulators and wireless fog/cloud computing support. Beside of its potential use in mitigating cybersickness, this device may have potential applications in augmented cognition ranging from feedback-controlled perceptual training to on-line learning and virtual social interactions. A prototype of the device has been made from a Samsung Gear VR for S6. This study explains its technical design to ensure precision data sampling, synchronous event marking, real-time signal processing and big data cloud computing support. This study also demonstrates the effective-ness in measuring the event-related potentials during a visual oddball experiment.
Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
(2014-06-03)
Zao, John K.
;
Gan, Tchin-Tze
;
You, Chun-Kai
;
Chung, Cheng-En
;
Wang, Yu-Te
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.
Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
(2014-06-03)
Zao, John K.
;
Gan, Tchin-Tze
;
You, Chun-Kai
;
Chung, Cheng-En
;
Wang, Yu-Te
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.
Pervasive Neuroimaging with Fog Computing and Linked Data
(2016-01-01)
Zao, John K.
;
Gan, Tchin-Tze
;
You, Chun-Kai
;
Mendez, Sergio Jose Rodriguez
;
You, Chieh
A pilot system for performing real-time brain imaging in naturalistic environments have been developed using wireless EEG headsets, motion sensors, smart telephones and ubiquitous computing servers. This paper described its pervasive architecture and introduced its enabling technologies, which include machine-to-machine publish/subscribe protocols, interoperable data/meta-data formats, multi-tier fog/cloud computing infrastructure and semantic linked data web. A live demonstration of this system was first performed at the US Army Research Lab meeting in March 2013. An expanded system capable of supporting real-time brain state classification and continuous model calibration will soon be made available as a web services.