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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

The 1-min plots of a player's concentration level during a 2-min open- eye relaxation period (left) and an equal-length open-eye concentration period (right).
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Figure 9: The 1-min plots of a player's concentration level during a 2-min open- eye relaxation period (left) and an equal-length open-eye concentration period (right).

Mentions: To demonstrate the working of our BCI processing pipeline, we showed in Figure 9 two 1-min scattered plots of a player's centration levels estimated during a 2-min open-eye relaxation period and an equal-length open-eye concentration period. The average concentration level during the relaxation period was μR = −0.19 < 0 as expected while the average level during the concentration period was μC = + 0.45. The difference between these values was statistically significant. The estimated values fluctuated notably during both periods. Partially, this was due to the wavering of player's concentration levels, but more likely, the fluctuations were caused by the remaining artifacts of head movements and muscle tension. These artifacts remain as an inevitable component of real-life EEG recording and a challenge to real-world BCI operation. Finally, both plots showed a general downward trend. This was because when the player tried to sustain her concentration, mental fatigue invariably set in after a short while; hence, her EEG power in beta band tended to decrease gradually relative to the power in alpha band. On the other hand, when the player tried to relax, it took some time for her to settle into a relaxed state; hence, we expect her alpha power to increase gradually relative to her beta power. In both cases, gradual decrease in concentration level was expected, especially if the player was untrained to perform the cognitive task.


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)

The 1-min plots of a player's concentration level during a 2-min open- eye relaxation period (left) and an equal-length open-eye concentration period (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: The 1-min plots of a player's concentration level during a 2-min open- eye relaxation period (left) and an equal-length open-eye concentration period (right).
Mentions: To demonstrate the working of our BCI processing pipeline, we showed in Figure 9 two 1-min scattered plots of a player's centration levels estimated during a 2-min open-eye relaxation period and an equal-length open-eye concentration period. The average concentration level during the relaxation period was μR = −0.19 < 0 as expected while the average level during the concentration period was μC = + 0.45. The difference between these values was statistically significant. The estimated values fluctuated notably during both periods. Partially, this was due to the wavering of player's concentration levels, but more likely, the fluctuations were caused by the remaining artifacts of head movements and muscle tension. These artifacts remain as an inevitable component of real-life EEG recording and a challenge to real-world BCI operation. Finally, both plots showed a general downward trend. This was because when the player tried to sustain her concentration, mental fatigue invariably set in after a short while; hence, her EEG power in beta band tended to decrease gradually relative to the power in alpha band. On the other hand, when the player tried to relax, it took some time for her to settle into a relaxed state; hence, we expect her alpha power to increase gradually relative to her beta power. In both cases, gradual decrease in concentration level was expected, especially if the player was untrained to perform the cognitive task.

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