title: Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals.
abstract: OBJECTIVE
  A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis.
  
  
  METHODS
  Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition.
  
  
  RESULTS
  The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition.
  
  
  CONCLUSIONS
  Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.
authors.affiliation: Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Kyoto, Japan; Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
authors.forename: Ryohei
authors.initials: R
authors.lastname: Fukuma
authors.affiliation: Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan; Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Kyoto, Japan; Division of Functional Diagnostic Science, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
authors.forename: Takufumi
authors.initials: T
authors.lastname: Yanagisawa
authors.affiliation: Division of Functional Diagnostic Science, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
authors.forename: Shiro
authors.initials: S
authors.lastname: Yorifuji
authors.affiliation: Division of Systems Research, Yokohama National University, Yokohama, Kanagawa, Japan.
authors.forename: Ryu
authors.initials: R
authors.lastname: Kato
authors.affiliation: Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, Chofu, Tokyo, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Yokoi
authors.affiliation: Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
authors.forename: Masayuki
authors.initials: M
authors.lastname: Hirata
authors.affiliation: Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan; Department of Neuromodulation and Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
authors.forename: Youichi
authors.initials: Y
authors.lastname: Saitoh
authors.affiliation: Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
authors.forename: Haruhiko
authors.initials: H
authors.lastname: Kishima
authors.affiliation: Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Seika-cho, Kyoto, Japan; Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan; Graduate School of Informatics, Kyoto University, Kyoto, Japan.
authors.forename: Yukiyasu
authors.initials: Y
authors.lastname: Kamitani
authors.affiliation: Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
authors.forename: Toshiki
authors.initials: T
authors.lastname: Yoshimine
mesh_terms: D000328:Adult; D000465:Algorithms; D001186:Artificial Limbs; D062207:Brain-Computer Interfaces; D005260:Female; D006225:Hand / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography; D008297:Male; D009044:Motor Cortex; D009068:Movement; D016011:Normal Distribution; D011474:Prosthesis Design; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D055815:Young Adult