Author: Alam, Md. Monzurul
Title: Brain-machine-muscle interfaces for restoring locomotion after spinal cord injuries
Degree: Ph.D.
Year: 2013
Subject: Brain-computer interfaces.
Spinal cord -- Wounds and injuries -- Treatment.
Hong Kong Polytechnic University -- Dissertations
Department: Department of Rehabilitation Sciences
Pages: x, 98 p. : ill. ; 30 cm.
Language: English
Abstract: Spinal Cord Injury (SCI) is a devastating neuronal dysfunction affecting a large population worldwide. Regaining lower-limb functionality such as walking is one of the highest priorities among all the disabilities of SCI paraplegics. Although the ultimate recovery would be repairing or regenerating new axons across the injured lesion potentially by stem cells or other transplants and neurotropic factors, long standing challenges to achieve this as well as recent technological advancements demand the development of neuroprosthetic devices to restore motor function following the injury. Brain-machine interface (BMI) is a neuroprosthetic approach for restoring motor function in paralysed patients. While BMI neuroprostheses have been successfully evaluated for restoring upper-limb functions, very little research has focused on developing such systems to restore lower-limb functions. This research study addresses the following questions: 1) whether different step gait-related neural information can be captured in parallel from rats' primary motor cortex during walking, and 2) whether and how this neural information can be utilized to restore locomotion after complete spinal transections. In the current study, spinal rats (mid-thoracic transection) were utilized as the animal model to design and develop a hindlimb BMI for locomotion. Neural signals recording were accomplished from the hindlimb area of the primary motor cortex (M1) to decode the "intent" of locomotive information during treadmill walking. The results show a strong association of neural activities with step gait cycles in healthy subjects. These neural activities dropped significantly following spinal transection. However, the locomotive states (standing or walking) could still be successfully decoded from these neural recordings. Finally, a novel BMI device was developed that processes this real-time neural information to electrically activate paralysed hindlimb muscles to mimic stepping. This study proposes lower-limb BMI as a future neuroprosthesis for SCI paraplegics.
Access: open access

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