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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

Fog and Cloud Computing architecture for multiplayer on-line EEG-BCI game.
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Figure 6: Fog and Cloud Computing architecture for multiplayer on-line EEG-BCI game.

Mentions: Figure 6 illustrates the system architecture for this game, which is also a typical configuration for multi-site interactive BCI operation. Each user has a typical BCI frontend (shown as a sky blue box) consisting of an EEG headset and a mobile phone that are connected to a local Fog Server (a navy blue box). The Fog Servers associated with different users may exchange information with one another and a Cloud Server (the green box). The game was running as a mobile application on each user's mobile phone, which serves mainly as a graphic user interface (GUI). Raw EEG data streams were sent directly to the Fog Server or through the mobile phones. Real-time signal processing and prediction were performed on the Fog Servers, each of which ran a BCI signal processing pipeline. The brain states of individual users were published by the Fog Servers and sent to the game running on each mobile phone, which subscribed for the brain state information.


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)

Fog and Cloud Computing architecture for multiplayer on-line EEG-BCI game.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Fog and Cloud Computing architecture for multiplayer on-line EEG-BCI game.
Mentions: Figure 6 illustrates the system architecture for this game, which is also a typical configuration for multi-site interactive BCI operation. Each user has a typical BCI frontend (shown as a sky blue box) consisting of an EEG headset and a mobile phone that are connected to a local Fog Server (a navy blue box). The Fog Servers associated with different users may exchange information with one another and a Cloud Server (the green box). The game was running as a mobile application on each user's mobile phone, which serves mainly as a graphic user interface (GUI). Raw EEG data streams were sent directly to the Fog Server or through the mobile phones. Real-time signal processing and prediction were performed on the Fog Servers, each of which ran a BCI signal processing pipeline. The brain states of individual users were published by the Fog Servers and sent to the game running on each mobile phone, which subscribed for the brain state information.

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