NDLI: Towards a non-invasive brain-machine interface system to restore gait function in humans

Content Provider	IEEE Xplore Digital Library
Author	Presacco, A. Forrester, L. Contreras-Vidal, J.L.
Copyright Year	2011
Description	Author affiliation: Department of Kinesiology, Department of Bioengineering and Graduate Program in Neuroscience and Cognitive Science University of Maryland, College Park, MD 20742, USA (Contreras-Vidal, J.L.) \|\| Department of Physical Therapy & Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD 21201 USA (Forrester, L.) \|\| Department of Kinesiology, University of Maryland, College Park, MD 20742, USA (Presacco, A.)
Abstract	Before 2009, the feasibility of applying brain-machine interfaces (BMIs) to control prosthetic devices had been limited to upper limb prosthetics such as the DARPA modular prosthetic limb. Until recently, it was believed that the control of bipedal locomotion involved central pattern generators with little supraspinal control. Analysis of cortical dynamics with electroencephalography (EEG) was also prevented by the lack of analysis tools to deal with excessive signal artifacts associated with walking. Recently, Nicolelis and colleagues paved the way for the decoding of locomotion showing that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to decode bipedal kinematics in rhesus monkeys. However, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This study uses non-invasive EEG signals to decode human walking in six nondisabled adults. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs, to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular kinematics of the left and right hip, knee and ankle joints and EEG were recorded concurrently. Our results support the possibility of decoding human bipedal locomotion with EEG. The average of the correlation values (r) between predicted and recorded kinematics for the six subjects was 0.7 (±0.12) for the right leg and 0.66 (±0.11) for the left leg. The average signal-to-noise ratio (SNR) values for the predicted parameters were 3.36 (±1.89) dB for the right leg and 2.79 (±1.33) dB for the left leg. These results show the feasibility of developing non-invasive neural interfaces for volitional control of devices aimed at restoring human gait function.
Starting Page	4588
Ending Page	4591
File Size	972477
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091136
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Decoding Kinematics Joints Legged locomotion Humans Band pass filters
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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