Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
Identifieur interne : 003059 ( Ncbi/Checkpoint ); précédent : 003058; suivant : 003060Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
Auteurs : John K. Zao [République populaire de Chine] ; Tchin-Tze Gan [République populaire de Chine] ; Chun-Kai You [République populaire de Chine] ; Cheng-En Chung [République populaire de Chine] ; Yu-Te Wang [États-Unis] ; Sergio José Rodríguez Méndez [République populaire de Chine] ; Tim Mullen [États-Unis] ; Chieh Yu [République populaire de Chine] ; Christian Kothe [États-Unis] ; Ching-Teng Hsiao [République populaire de Chine] ; San-Liang Chu [République populaire de Chine] ; Ce-Kuen Shieh [République populaire de Chine] ; Tzyy-Ping Jung [États-Unis]Source :
- Frontiers in Human Neuroscience [ 1662-5161 ] ; 2014.
Abstract
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.
Url:
DOI: 10.3389/fnhum.2014.00370
PubMed: 24917804
PubMed Central: 4042686
Affiliations:
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PMC:4042686Le document en format XML
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<author><name sortKey="Gan, Tchin Tze" sort="Gan, Tchin Tze" uniqKey="Gan T" first="Tchin-Tze" last="Gan">Tchin-Tze Gan</name>
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<author><name sortKey="Rodriguez Mendez, Sergio Jose" sort="Rodriguez Mendez, Sergio Jose" uniqKey="Rodriguez Mendez S" first="Sergio José" last="Rodríguez Méndez">Sergio José Rodríguez Méndez</name>
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<author><name sortKey="Hsiao, Ching Teng" sort="Hsiao, Ching Teng" uniqKey="Hsiao C" first="Ching-Teng" last="Hsiao">Ching-Teng Hsiao</name>
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<front><div type="abstract" xml:lang="en"><p>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.</p>
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</div1>
</back>
</TEI>
<affiliations><list><country><li>République populaire de Chine</li>
<li>États-Unis</li>
</country>
</list>
<tree><country name="République populaire de Chine"><noRegion><name sortKey="Zao, John K" sort="Zao, John K" uniqKey="Zao J" first="John K." last="Zao">John K. Zao</name>
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</tree>
</affiliations>
</record>
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