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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

Time traces of end-to-end synchronous transport of motion and EEG data streams. (A,B) show the time traces of motion and EEG data transports in two separate sessions. (C,D) show the traces of both transports in the same session. The blue lines mark the traces of transmission time while the red lines mark those of reception time. Their slopes give the average transmission and reception intervals of individual messages.
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Figure 5: Time traces of end-to-end synchronous transport of motion and EEG data streams. (A,B) show the time traces of motion and EEG data transports in two separate sessions. (C,D) show the traces of both transports in the same session. The blue lines mark the traces of transmission time while the red lines mark those of reception time. Their slopes give the average transmission and reception intervals of individual messages.

Mentions: Detail timing measurements of the end-to-end synchronous transports were made later in August during several replay of the demonstration and analyzed off time. Figure 5 shows the time traces of standalone and concurrent transport of the two data streams. Table 2 lists the formats and sizes of individual messages as well as the statistics of timing measurements of the transports. The significant differences in the mean values of transport latency were due to the offsets existing between the system clocks in the mobile phone at NCTU and the desktop computer at UCSD.


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)

Time traces of end-to-end synchronous transport of motion and EEG data streams. (A,B) show the time traces of motion and EEG data transports in two separate sessions. (C,D) show the traces of both transports in the same session. The blue lines mark the traces of transmission time while the red lines mark those of reception time. Their slopes give the average transmission and reception intervals of individual messages.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Time traces of end-to-end synchronous transport of motion and EEG data streams. (A,B) show the time traces of motion and EEG data transports in two separate sessions. (C,D) show the traces of both transports in the same session. The blue lines mark the traces of transmission time while the red lines mark those of reception time. Their slopes give the average transmission and reception intervals of individual messages.
Mentions: Detail timing measurements of the end-to-end synchronous transports were made later in August during several replay of the demonstration and analyzed off time. Figure 5 shows the time traces of standalone and concurrent transport of the two data streams. Table 2 lists the formats and sizes of individual messages as well as the statistics of timing measurements of the transports. The significant differences in the mean values of transport latency were due to the offsets existing between the system clocks in the mobile phone at NCTU and the desktop computer at UCSD.

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