GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Kishima, Haruhiko  (3)
  • 2020-2024  (3)
  • 1
    In: The Journal of Pain, Elsevier BV, Vol. 23, No. 12 ( 2022-12), p. 2080-2091
    Type of Medium: Online Resource
    ISSN: 1526-5900
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2040378-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Neurology, Ovid Technologies (Wolters Kluwer Health), Vol. 95, No. 4 ( 2020-07-28), p. e417-e426
    Abstract: To determine whether training with a brain–computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain. Methods Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days (“real training”). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values (“random training”). The 2 trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608). Results VAS at day 4 was significantly reduced from the baseline after real training (mean [SD], 45.3 [24.2] –30.9 [20.6], 1/100 mm; p = 0.009 〈 0.025), but not after random training ( p = 0.047 〉 0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training ( p 〈 0.01). Conclusion Three-day training to move the hand images controlled by BCI significantly reduced pain for 1 week. Classification of evidence This study provides Class III evidence that BCI reduces phantom limb pain.
    Type of Medium: Online Resource
    ISSN: 0028-3878 , 1526-632X
    RVK:
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    In: Journal of Neural Engineering, IOP Publishing, Vol. 17, No. 3 ( 2020-06-01), p. 036009-
    Abstract: Objective. Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotemporal patterns characterizing movement types to increase the amount of information gained from these signals. Approach . Previous studies have demonstrated that motor information could be decoded using powers of specific frequency bands of the ECoG signals estimated by fast Fourier transform (FFT) or wavelet analysis. However, because FFT is evaluated for each channel, the temporal and spatial patterns among channels are difficult to evaluate. Here, we used dynamic mode decomposition (DMD) to evaluate the spatiotemporal pattern of ECoG signals and evaluated the accuracy of motor decoding with the DMD modes. We used ECoG signals during three types of hand movements, which were recorded from 11 patients implanted with subdural electrodes. From the signals at the time of the movements, the modes and powers were evaluated by DMD and FFT and were decoded using support vector machine. We used the Grassmann kernel to evaluate the distance between modes estimated by DMD (DMD mode). In addition, we decoded the DMD modes, in which the phase components were shuffled, to compare the classification accuracy. Main results. The decoding accuracy using DMD modes was significantly better than that using FFT powers. The accuracy significantly decreased when the phases of the DMD mode were shuffled. Among the frequency bands, the DMD mode at approximately 100 Hz demonstrated the highest classification accuracy. Significance. DMD successfully captured the spatiotemporal patterns characterizing the movement types and contributed to improving the decoding accuracy. This method can be applied to improve BCIs to help severely paralyzed patients communicate.
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2135187-9
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...