Publication Date

2016-03-31

Availability

Embargoed

Embargo Period

2017-03-31

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Biomedical Engineering (Engineering)

Date of Defense

2016-03-22

First Committee Member

Abhishek Prasad

Second Committee Member

Jorge Bohorquez

Third Committee Member

Justin C. Sanchez

Fourth Committee Member

Kim Anderson

Fifth Committee Member

Joyce Gomes-Osman

Abstract

There are over 33,000 people in the United States living with complete tetraplegia due to traumatic spinal cord injury (SCI). These individuals rely heavily on family and caregivers as they are unable to perform many activities of daily living. People with complete tetraplegia rank restoration of hand and arm function as their highest priority, as it would offer greater independence and improved quality of life. In this study, we show that subjects with chronic (>1-year post-injury) C5/C6-level, motor-complete SCI are able to control a brain computer interface-functional electrical stimulation (BCI-FES) system to perform a hand grasp and release task. Electroencephalographic (EEG) signals were acquired using a 20-channel wireless EEG system and input to a BCI, which enabled autonomous control over FES of paralyzed muscles for hand grasp and release. A novel stimulation configuration and control paradigms were developed in order to provide reliable activation of the muscles responsible for hand movements. Input features and decoding strategies were evaluated from subjects with SCI, as well as uninjured, control subjects. After optimization of the BCI-FES system and experimental paradigm, 5 subjects with C5/C6, motor complete spinal cord injury and 5 uninjured, control subjects participated in 6 sessions of closed-loop BCI-FES. Subjects were asked to imagine opening and closing their right hand during the trials for motor imagery. Average power in 5 Hz bins (5-35 Hz) was extracted from C3, C1, Cz, C2, and C4 electrodes and input as features to a Support Vector Machine classification algorithm. When “movement intention” was classified correctly from the motor imagery period, a custom stimulation sequence was delivered to the forearm muscles via surface electrodes to enable opening and closing of the hand for grasp and release. Spinal cord injured subjects produced an average of 21.0% ± 3.9% event-related desynchronization and control subjects averaged 13.5% ± 3.2%. Average decoding accuracy was similar, at 73.3% ± 5.6% in the spinal cord injury group and 73.6% ± 3.8% in the control group. Over the course of experiments, average event-related desynchronization increased significantly in the SCI group and decoding accuracy improved. This study demonstrates that subjects with motor complete, cervical SCI were able to control a BCI-FES system with performance levels as high as healthy controls with minimal training. Non-invasive BCI-FES systems may have the potential to restore hand function in people with motor-complete SCI, which would increase independence and improve quality of life.

Keywords

Neuroprosthetics, Spinal Cord Injury, Brain-Computer Interface, Functional Electrical Stimulation, EEG, Hand Function

Share

COinS