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  1. AU="Li, Peirang"
  2. AU="Zhang, Zhao-Liang"
  3. AU="Perner, Sven"
  4. AU=Suwanwongse Kulachanya AU=Suwanwongse Kulachanya
  5. AU="Rose, Jacqueline"
  6. AU="E Lostis"

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  1. Artikel ; Online: Exploring Neurofeedback Training for BMI Power Augmentation of Upper Limbs: A Pilot Study.

    Liang, Hongbo / Maedono, Shota / Yu, Yingxin / Liu, Chang / Ueda, Naoya / Li, Peirang / Zhu, Chi

    Entropy (Basel, Switzerland)

    2021  Band 23, Heft 4

    Abstract: Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) ...

    Abstract Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12-40 Hz) power increased after the NFB training for both the elbow and the shoulder joints' movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.
    Sprache Englisch
    Erscheinungsdatum 2021-04-09
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e23040443
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Functional Evaluation of a Force Sensor-Controlled Upper-Limb Power-Assisted Exoskeleton with High Backdrivability.

    Liu, Chang / Liang, Hongbo / Ueda, Naoya / Li, Peirang / Fujimoto, Yasutaka / Zhu, Chi

    Sensors (Basel, Switzerland)

    2020  Band 20, Heft 21

    Abstract: A power-assisted exoskeleton should be capable of reducing the burden on the wearer's body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance ... ...

    Abstract A power-assisted exoskeleton should be capable of reducing the burden on the wearer's body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer's movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer's upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton's ability to support the wearer's movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer's surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer's movements.
    Mesh-Begriff(e) Biomechanical Phenomena ; Electromyography ; Exoskeleton Device ; Humans ; Movement ; Range of Motion, Articular ; Upper Extremity
    Sprache Englisch
    Erscheinungsdatum 2020-11-09
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s20216379
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: Feature Extraction of Shoulder Joint's Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance.

    Liang, Hongbo / Zhu, Chi / Iwata, Yu / Maedono, Shota / Mochita, Mika / Liu, Chang / Ueda, Naoya / Li, Peirang / Yu, Haoyong / Yan, Yuling / Duan, Feng

    Bioengineering (Basel, Switzerland)

    2018  Band 6, Heft 1

    Abstract: Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals' daily lives to use their brain activity information instead of their bodies. In order to reduce costs and ... ...

    Abstract Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals' daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals' features of the shoulder joint, which is the most complex joint in the human's body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300⁻400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously.
    Sprache Englisch
    Erscheinungsdatum 2018-12-24
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2746191-9
    ISSN 2306-5354
    ISSN 2306-5354
    DOI 10.3390/bioengineering6010002
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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