NDLI: Motion classification using epidural electrodes for low-invasive brain-machine interface

Content Provider	IEEE Xplore Digital Library
Author	Uejima, T. Kita, K. Fujii, T. Kato, R. Takita, M. Yokoi, H.
Copyright Year	2009
Description	Author affiliation: Institute for Human Science and Biomedical Engineering, National Institute of Advanced Industrial Science and Technology, Japan (Takita, M.) \|\| Department of Precision Engineering, The University of Tokyo, Japan. He is now with Olympus Medical Systems Corp., Japan (Fujii, T.) \|\| Department of Precision Engineering, The University of Tokyo, Japan (Uejima, T.; Kita, K.; Kato, R.; Yokoi, H.)
Abstract	Brain-machine interfaces (BMIs) are expected to be used to assist seriously disabled persons’ communications and reintegrate their motor functions. One of the difficult problems to realize practical BMI is how to record neural activity clearly and safely. Conventional invasive methods require electrodes inside the dura mater, and noninvasive methods do not involve surgery but have poor signal quality. Thus a low-invasive method of recording is important for safe and practical BMI. In this study, the authors used epidural electrodes placed between the skull and dura mater to record a rat’s neural activity for low-invasive BMI. The signals were analyzed using a short-time Fourier transform, and the power spectra were classified into rat motions by a support vector machine. Classification accuracies were up to 96% in two-class discrimination, including that when the rat stopped, walked, and rested. The feasibility of a low-invasive BMI based on an epidural neural recording was shown in this study.
Starting Page	6469
Ending Page	6472
File Size	858113
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5333547
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Electroencephalography Data mining Precision engineering Brain Spatial resolution Prosthetics Surgery Skull Signal analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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