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Grant: Research Tools and Instruments

Queen's University

$150,000.00

Detecting brain signals using electroencephalography and functional near infrared spectroscopy

FY 2016-17

Summary
The requested fNIR/EEG system (functional near infrared spectroscopy/electroencephalography) system will provide a number of multidisciplinary researchers the opportunity to map neural function to better predict effective electrode placement when interacting with brain computer interfaces. Brain computer interfaces are those systems that allow an individual to use a computer with signals collected from the surface of the scalp. While EEG can detect signals quickly and accurately, it is difficult to identify the underlying neural regions that contribute to the signal during specific tasks targeted to motor, cognitive and sensory control. Previous research has shown that individuals with neurological impairment have altered EEG signals and we have found that this inhibits effective use with a brain computer interface. The system requested can be used to map the EEG and the fNIR signals at the same location allowing a better mapping of the locations of neural activity. The placement of EEG electrodes for brain computer interfaces can then be targeted to detect repeatable brain wave signals. This method is revolutionary in identifying EEG electrode placement and will increase the successful use of brain computer interfaces. The requested system will be housed at the Building and Designing Assistive Technology Lab located at the Kingston General Hospital. This lab is collocated with the KINARM robot. This robot is an exoskeleton which is used with virtual reality gaming to evaluate function in neurological populations. The KINARM will be used in combination with this fNIR/EEG system to identify regions of neural activity specific to motor, cognitive and sensory tasks. With individualized neural imagery and customized mapping algorithms, targeted EEG electrode placement will allow engagement with brain computer interfaces. For populations who have no other form of interacting with the environment such as those with high level impairment in the form of cerebral palsy, ALS, or quadriplegia, computer access allows communication and active engagement with the community. We believe this is the first attempt worldwide to target specific electrode positions based on neural imagery for neurological populations.
Program
Research Tools and Instruments
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RTI-2017-00137
Name nom
Davies, Theresa
Department département
Mechanical and Materials Engineering - Mechanical and Materials Engineering
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2016
Competition year année de concours
2017
Award amount
150000
Program id
RTI
Program name en
Research Tools and Instruments
Program name fr
Outils et instruments de recherche
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1609
Committee name en
Engineering (RTI)
Committee name fr
Génie (OIR)
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
1901
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical technology
Research subject fr
Technologie biomédicale
Partie
1
Nb partie
1
Application title
Detecting brain signals using electroencephalography and functional near infrared spectroscopy
Keyword
NSERC
Application summary
The requested fNIR/EEG system (functional near infrared spectroscopy/electroencephalography) system will provide a number of multidisciplinary researchers the opportunity to map neural function to better predict effective electrode placement when interacting with brain computer interfaces. Brain computer interfaces are those systems that allow an individual to use a computer with signals collected from the surface of the scalp. While EEG can detect signals quickly and accurately, it is difficult to identify the underlying neural regions that contribute to the signal during specific tasks targeted to motor, cognitive and sensory control. Previous research has shown that individuals with neurological impairment have altered EEG signals and we have found that this inhibits effective use with a brain computer interface. The system requested can be used to map the EEG and the fNIR signals at the same location allowing a better mapping of the locations of neural activity. The placement of EEG electrodes for brain computer interfaces can then be targeted to detect repeatable brain wave signals. This method is revolutionary in identifying EEG electrode placement and will increase the successful use of brain computer interfaces. The requested system will be housed at the Building and Designing Assistive Technology Lab located at the Kingston General Hospital. This lab is collocated with the KINARM robot. This robot is an exoskeleton which is used with virtual reality gaming to evaluate function in neurological populations. The KINARM will be used in combination with this fNIR/EEG system to identify regions of neural activity specific to motor, cognitive and sensory tasks. With individualized neural imagery and customized mapping algorithms, targeted EEG electrode placement will allow engagement with brain computer interfaces. For populations who have no other form of interacting with the environment such as those with high level impairment in the form of cerebral palsy, ALS, or quadriplegia, computer access allows communication and active engagement with the community. We believe this is the first attempt worldwide to target specific electrode positions based on neural imagery for neurological populations.
Queen's University - $150,000 Grant (2016-17) | Canada Spends