Limits...
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

An EEG Tractor Beam game session with four people playing over the Internet: two players at SCCN in San Diego, USA are shown in the foreground while two other players at NCTU in Hsinchu, Taiwan appear in the monitor display. The inset at the lower right corner shows a captured view of the game display.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4042686&req=5

Figure 7: An EEG Tractor Beam game session with four people playing over the Internet: two players at SCCN in San Diego, USA are shown in the foreground while two other players at NCTU in Hsinchu, Taiwan appear in the monitor display. The inset at the lower right corner shows a captured view of the game display.

Mentions: Where the PSDs are the average power spectral density in α, β and θ bands of the player. In order to win the game, a player should try to pull the target toward herself while depriving other players their chances to grab the target. The game implements a “winner-take-all” strategy: a player is awarded points at a rate proportional to the percentage of total attractive force she exerts on the target, which is calculated by dividing that player's concentration level by the sum of the levels among all the players. However, a player can only start to accumulate points if she contributes at least her fair share to the total sum. A tractor beam will appear between that player and the target when her concentration level passes that threshold. That was when she starts to cumulate her points. Figure 7 shows a picture of four players engaging in the game across the Pacific Ocean.


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)

An EEG Tractor Beam game session with four people playing over the Internet: two players at SCCN in San Diego, USA are shown in the foreground while two other players at NCTU in Hsinchu, Taiwan appear in the monitor display. The inset at the lower right corner shows a captured view of the game display.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: An EEG Tractor Beam game session with four people playing over the Internet: two players at SCCN in San Diego, USA are shown in the foreground while two other players at NCTU in Hsinchu, Taiwan appear in the monitor display. The inset at the lower right corner shows a captured view of the game display.
Mentions: Where the PSDs are the average power spectral density in α, β and θ bands of the player. In order to win the game, a player should try to pull the target toward herself while depriving other players their chances to grab the target. The game implements a “winner-take-all” strategy: a player is awarded points at a rate proportional to the percentage of total attractive force she exerts on the target, which is calculated by dividing that player's concentration level by the sum of the levels among all the players. However, a player can only start to accumulate points if she contributes at least her fair share to the total sum. A tractor beam will appear between that player and the target when her concentration level passes that threshold. That was when she starts to cumulate her points. Figure 7 shows a picture of four players engaging in the game across the Pacific Ocean.

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