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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

Brain state estimation pipeline used in EEG Tractor Beam game.
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Figure 8: Brain state estimation pipeline used in EEG Tractor Beam game.

Mentions: The necessary EEG signal processing and the estimation of concentration level ∁ were performed by the BCILAB/SIFT pipeline (Delorme et al., 2011) running on MATLAB R2013a (Mathworks, 2013) installed in the Fog Servers. Figure 8 displays the typical processing stages of this brain state estimation pipeline. Its MATLAB code was included in the Appendix for reference. The EEG preprocessing stage aims at cleaning up the raw EEG signals, which was heavily contaminated by artifacts due to eye blinks and head movements. The heavy computation of signal correlation and artifact subspace reconstruction (Mullen et al., 2012) can only be performed on the Fog Servers; these algorithms can quickly drain the batteries in the sensors and the mobile phones. Because players' concentration levels was estimated as the ratios between power spectral density in different EEG frequency bands, multitaper spectral estimation, power density calibration1 and averaging were done before the concentration levels were computed. Please note that although we chose to implement the BCI processing pipeline using BCILAB and SIFT, other real-time signal processing software can be used to perform the computation.


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)

Brain state estimation pipeline used in EEG Tractor Beam game.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Brain state estimation pipeline used in EEG Tractor Beam game.
Mentions: The necessary EEG signal processing and the estimation of concentration level ∁ were performed by the BCILAB/SIFT pipeline (Delorme et al., 2011) running on MATLAB R2013a (Mathworks, 2013) installed in the Fog Servers. Figure 8 displays the typical processing stages of this brain state estimation pipeline. Its MATLAB code was included in the Appendix for reference. The EEG preprocessing stage aims at cleaning up the raw EEG signals, which was heavily contaminated by artifacts due to eye blinks and head movements. The heavy computation of signal correlation and artifact subspace reconstruction (Mullen et al., 2012) can only be performed on the Fog Servers; these algorithms can quickly drain the batteries in the sensors and the mobile phones. Because players' concentration levels was estimated as the ratios between power spectral density in different EEG frequency bands, multitaper spectral estimation, power density calibration1 and averaging were done before the concentration levels were computed. Please note that although we chose to implement the BCI processing pipeline using BCILAB and SIFT, other real-time signal processing software can be used to perform the computation.

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