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Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

Zao JK, Gan TT, You CK, Chung CE, Wang YT, Rodríguez Méndez SJ, Mullen T, Yu C, Kothe C, Hsiao CT, Chu SL, Shieh CK, Jung TP - Front Hum Neurosci (2014)

Bottom Line: 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 shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

View Article: PubMed Central - PubMed

Affiliation: Pervasive Embedded Technology Lab, Computer Science Department, National Chiao Tung University Hsinchu, Taiwan, R.O.C.

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.

No MeSH data available.


Related in: MedlinePlus

Conceptual architecture of Fog/Cloud Computing infrastructure.
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Figure 1: Conceptual architecture of Fog/Cloud Computing infrastructure.

Mentions: Figure 1 illustrates the concept of multi-tier Fog and Cloud Computing. The first tier, known as the front-end, consists of battery-powered wireless sensors and mobile devices, which serve as the interfaces between the physical world, the human users and the cybernetic information infrastructure. The second tier or the near-end is formed by an ad-hoc conglomerate of consumer IT products such as personal computers, television set-top boxes, and game consoles close to the front-end devices over the Internet. These computing nodes, known as the Fog Servers, have sufficient electric power, data storage, and computing capacity to offload the computing burden from the front-end devices in order to prolong their battery lives and enhance their performance. The final tier or the far-end is made up of Cloud Servers installed in public or private data centers. These high-performance computers not only have plenty computing power, storage capacity and communication bandwidth; they have also accumulated vast amount of information and can use them to make deduction and prediction beyond the capability of stand-alone computers. This massive Cloud-based information warehouse and computing engine is the “backbone” of this distributed infrastructure. Sophisticated as it seems, the Fog/Cloud Computing infrastructure is expected to be widely deployed riding the tie of the Internet-of-Things. For examples, the smart homes and buildings will have smart electric meters that can control the power consumption of electric appliances while interacting with the smart power grids; the in-home multimedia servers will deliver bundled information and communication services from the “Internet cloud” to individuals' personal devices; intelligent transportation systems will install roadside controllers/servers that will interact with pedestrians' mobile phones and vehicles' on-board computers while pulling and pushing data to the municipal and national data centers. From this perspective, our on-line EEG-BCI systems can be regarded as a kind of pervasive personal telemonitoring system. Consequently, all our design decisions were made to ensure interoperability with the de-facto or emerging standards in the field of machine-to-machine communication and Internet-of-Things.


Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

Zao JK, Gan TT, You CK, Chung CE, Wang YT, Rodríguez Méndez SJ, Mullen T, Yu C, Kothe C, Hsiao CT, Chu SL, Shieh CK, Jung TP - Front Hum Neurosci (2014)

Conceptual architecture of Fog/Cloud Computing infrastructure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4042686&req=5

Figure 1: Conceptual architecture of Fog/Cloud Computing infrastructure.
Mentions: Figure 1 illustrates the concept of multi-tier Fog and Cloud Computing. The first tier, known as the front-end, consists of battery-powered wireless sensors and mobile devices, which serve as the interfaces between the physical world, the human users and the cybernetic information infrastructure. The second tier or the near-end is formed by an ad-hoc conglomerate of consumer IT products such as personal computers, television set-top boxes, and game consoles close to the front-end devices over the Internet. These computing nodes, known as the Fog Servers, have sufficient electric power, data storage, and computing capacity to offload the computing burden from the front-end devices in order to prolong their battery lives and enhance their performance. The final tier or the far-end is made up of Cloud Servers installed in public or private data centers. These high-performance computers not only have plenty computing power, storage capacity and communication bandwidth; they have also accumulated vast amount of information and can use them to make deduction and prediction beyond the capability of stand-alone computers. This massive Cloud-based information warehouse and computing engine is the “backbone” of this distributed infrastructure. Sophisticated as it seems, the Fog/Cloud Computing infrastructure is expected to be widely deployed riding the tie of the Internet-of-Things. For examples, the smart homes and buildings will have smart electric meters that can control the power consumption of electric appliances while interacting with the smart power grids; the in-home multimedia servers will deliver bundled information and communication services from the “Internet cloud” to individuals' personal devices; intelligent transportation systems will install roadside controllers/servers that will interact with pedestrians' mobile phones and vehicles' on-board computers while pulling and pushing data to the municipal and national data centers. From this perspective, our on-line EEG-BCI systems can be regarded as a kind of pervasive personal telemonitoring system. Consequently, all our design decisions were made to ensure interoperability with the de-facto or emerging standards in the field of machine-to-machine communication and Internet-of-Things.

Bottom Line: 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 shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

View Article: PubMed Central - PubMed

Affiliation: Pervasive Embedded Technology Lab, Computer Science Department, National Chiao Tung University Hsinchu, Taiwan, R.O.C.

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.

No MeSH data available.


Related in: MedlinePlus