Browsing by Author "Mullen, Tim"

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Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving
(2016-02-17)
Lin, Chin-Teng
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Chuang, Chun-Hsiang
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Kerick, Scott
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Mullen, Tim
;
Jung, Tzyy-Ping
Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory modalities as the driver detected the lane-departure in a simulated driving task would promote a shift of brain activity between different modes of processing, reflected by brain network dynamics on electroencephalographic sources. Results showed that depriving the driver of salient sensory information imposes a relatively more perceptually-demanding task, leading to a stronger activation in the task-positive network. When the vehicle motion feedback is available, the drivers may rely on vehicle motion to perceive the perturbations, which frees attentional capacity and tends to activate the default mode network. Such brain network dynamics could have major implications for understanding fluctuations in driver attention and designing advance driver assistance systems.
Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
(2014-06-03)
Zao, John K.
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Gan, Tchin-Tze
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You, Chun-Kai
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Chung, Cheng-En
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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.
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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.