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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

Alignments between the proposed BCI Ontology and the SSN Stimuli-Sensor-Observation ontology design pattern. The directed graph depicts the relations (edges) among the cores concepts/classes (rounded-square nodes) from different namespaces including the default BCI namespace (sky-blue colored nodes), the SSN namespace (colored nodes with ssn prefix), and the Dbpedia namespace (tan colored nodes with dbp prefix). The sub-graph with red outlines contains the basic SSN concepts. The rest of the graph shows how the concepts such as Subject, BciSession, BciRecord, BciDevice, Resource, and HED are aligned with the concepts of Stimuli, Sensor, and Observations (dark-blue nodes) in the design pattern. For example, the class BciDevice in the BCI namespace is a subclass of SensingDevice in the SSN namespace, which in turn is a subclass of Sensor in the SSN ontology design pattern.
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Figure 2: Alignments between the proposed BCI Ontology and the SSN Stimuli-Sensor-Observation ontology design pattern. The directed graph depicts the relations (edges) among the cores concepts/classes (rounded-square nodes) from different namespaces including the default BCI namespace (sky-blue colored nodes), the SSN namespace (colored nodes with ssn prefix), and the Dbpedia namespace (tan colored nodes with dbp prefix). The sub-graph with red outlines contains the basic SSN concepts. The rest of the graph shows how the concepts such as Subject, BciSession, BciRecord, BciDevice, Resource, and HED are aligned with the concepts of Stimuli, Sensor, and Observations (dark-blue nodes) in the design pattern. For example, the class BciDevice in the BCI namespace is a subclass of SensingDevice in the SSN namespace, which in turn is a subclass of Sensor in the SSN ontology design pattern.

Mentions: The core of SSN Ontology is the Stimulus-Sensor-Observation Ontology Design Pattern (Compton and Janowicz, 2010) built upon the basic concepts of stimuli, sensor and observations. The sub-graph marked with the red outlines in Figure 2 is the semantic graph of this design pattern.


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)

Alignments between the proposed BCI Ontology and the SSN Stimuli-Sensor-Observation ontology design pattern. The directed graph depicts the relations (edges) among the cores concepts/classes (rounded-square nodes) from different namespaces including the default BCI namespace (sky-blue colored nodes), the SSN namespace (colored nodes with ssn prefix), and the Dbpedia namespace (tan colored nodes with dbp prefix). The sub-graph with red outlines contains the basic SSN concepts. The rest of the graph shows how the concepts such as Subject, BciSession, BciRecord, BciDevice, Resource, and HED are aligned with the concepts of Stimuli, Sensor, and Observations (dark-blue nodes) in the design pattern. For example, the class BciDevice in the BCI namespace is a subclass of SensingDevice in the SSN namespace, which in turn is a subclass of Sensor in the SSN ontology design pattern.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Alignments between the proposed BCI Ontology and the SSN Stimuli-Sensor-Observation ontology design pattern. The directed graph depicts the relations (edges) among the cores concepts/classes (rounded-square nodes) from different namespaces including the default BCI namespace (sky-blue colored nodes), the SSN namespace (colored nodes with ssn prefix), and the Dbpedia namespace (tan colored nodes with dbp prefix). The sub-graph with red outlines contains the basic SSN concepts. The rest of the graph shows how the concepts such as Subject, BciSession, BciRecord, BciDevice, Resource, and HED are aligned with the concepts of Stimuli, Sensor, and Observations (dark-blue nodes) in the design pattern. For example, the class BciDevice in the BCI namespace is a subclass of SensingDevice in the SSN namespace, which in turn is a subclass of Sensor in the SSN ontology design pattern.
Mentions: The core of SSN Ontology is the Stimulus-Sensor-Observation Ontology Design Pattern (Compton and Janowicz, 2010) built upon the basic concepts of stimuli, sensor and observations. The sub-graph marked with the red outlines in Figure 2 is the semantic graph of this design pattern.

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