A Gait Rehabilitation System for Tetraplegic Patients
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1
Korea University, Department of Brain and Cognitive Engineering, Republic of Korea
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2
Technische Universität Berlin, Machine Learning Group, Germany
In this paper, we propose a brain-machine interface (BMI) control system based on steady-state
visual evoked potentials (SSVEPs) for tetraplegic patients. To decode user intentions in real-time, a canonical correlation analysis (CCA) method was used for the extraction of frequency
information associated with the SSVEPs. Overall, 11 subjects participated in the experiment to
evaluate performance. Our results exhibit accuracies of 91.3% and response time of 3.28±1.82s and completion time of 1100 s for the experimental parcour studied. The ability to achieve such high quality BMI control indicates that SSVEP-based lower limb exoskeleton for gait assist
are becoming feasible, despite the high amount of artifacts that exoskeletons induce in the
EEG measurements. The SSVEP-based system requires only patient’s ocular movements and
attentions for visual stimuli. Hence, the system can be an efficient method for gait rehabilitation
with high performance.
Keywords:
Tetraplegic,
Rehabilitation,
brain-machine interface,
steady state visually evoked potentials (ssVEP),
canonical correlation analysis
Conference:
2015 International Workshop on Clinical Brain-Machine Interfaces (CBMI2015), Tokyo, Japan, 13 Mar - 15 Mar, 2015.
Presentation Type:
Poster 4-6
Topic:
Clinical Brain-Machine Interfaces
Citation:
Kwak
N,
Kim
K,
Mueller
KR and
Lee
S
(2015). A Gait Rehabilitation System for Tetraplegic Patients.
Conference Abstract:
2015 International Workshop on Clinical Brain-Machine Interfaces (CBMI2015).
doi: 10.3389/conf.fnhum.2015.218.00006
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Received:
23 Apr 2015;
Published Online:
29 Apr 2015.
*
Correspondence:
Dr. Seong-Whan Lee, Korea University, Department of Brain and Cognitive Engineering, Seongbuk-ku, Seoul, Republic of Korea, sw.lee@korea.ac.kr