Neuroprosthetic Control of an Anthropomorphic Exoskeleton in Tetraplegics
Neuroprosthetic Control of an Anthropomorphic Exoskeleton in Tetraplegics
批准号:
8955268
负责人:
Karunesh Ganguly
金额:
$237.75万
依托单位国家:
美国
项目类别:
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-09-30 至 2020-08-31
关键词:
Amyotrophic Lateral SclerosisBrainClinical ResearchDisabled PersonsElectrocorticogramEsthesiaFeedbackLearningLimb structureMovementNeurologicParalysedPatient CarePatientsProprioceptionProsthesisQuadriplegiaResearchResearch ProposalsResidual stateSignal TransductionSpinal cord injurySurveysSystemTechnologyTestingTranslatingTranslationsUpper Extremityarmbasebrain machine interfaceexoskeletongraspinnovationmotor function recoveryneuroprosthesispublic health relevancerelating to nervous systemrestorationsensory feedback
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): Patients with tetraplegia, or paralysis of all four limbs, are severely disabled. Surveys have found that restoration of upper-limb function is a high-priority for such patients. Brain-Machine Interfaces (BMIs) can eventually restore upper-limb reaching and grasping function by seamlessly merging the computational power of the brain with artificial prosthetic systems. A major challenge is robust translation of BMI technology
to patient care. Two well-recognized limitations of current approaches are instability of recordings and the lack of proprioceptive feedback signals. This research proposal aims to conduct a pilot clinical study to test electrocorticography (ECoG) based control of an anthropomorphic exoskeleton in tetraplegic patients with residual proprioception. We specifically seek to translate BMI technology to the significant subset of tetraplegic patients with intact sensation (e.g. amyotrophic lateral sclerosis or incomplete spinal cord injury). Our approach would capitalize on both the well-recognized stability of ECoG recordings and the natural sensory feedback generated by passive movements of the subject's arm by the exoskeleton. The proposed research should greatly advance translational efforts by optimizing control under conditions that maximize neural learning mechanisms and provide natural sensory feedback.
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1002/acn3.368
发表时间:
2016-12
期刊:
Annals of clinical and translational neurology
影响因子:
5.3
作者:
[Godlove J, Gulati T, Dichter B, Chang E, Ganguly K]
通讯作者:
Ganguly K
DOI:
10.1016/j.nbd.2015.07.015
发表时间:
2015-11
期刊:
Neurobiology of disease
影响因子:
6.1
作者:
[Tsu AP, Burish MJ, GodLove J, Ganguly K]
通讯作者:
Ganguly K
Detecting Movement Onset During Closed-Loop Stimulation Using A Hidden Markov Model.
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依托单位:
国内基金
海外基金
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依托单位: