Hand Exoskeleton Control for Cerebrum Plasticity Training Based on Brain–Computer Interface

Bi, Qian; Yang, Canjun; Yang, Wei; Fan, Jinchang; Wang, Hansong

doi:10.1007/978-981-10-2404-7_31

Cited by 2 publications

(2 citation statements)

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“…From all included studies, 16 used EEG-based systems based on motor imagery (Pfurtscheller et al, 2000 ; Webb et al, 2012 ; Xiao et al, 2014 ; Soekadar et al, 2015 ; Bi et al, 2017 ; Cantillo-Negrete et al, 2018 ; Kapsalyamov et al, 2019 ; Zhang et al, 2019 ; Badesa et al, 2020 ; Araujo et al, 2021 ), motor execution (Fok et al, 2011 ; King et al, 2014 ; Lee et al, 2017 ; Chowdhury et al, 2018 ), SSVEP (Ortner et al, 2011 ), or ERP (Pedrocchi et al, 2013 ; Delijorge et al, 2020 ) to control the ULO. One included study by Lee et al ( 2017 ) exploited brain activation not directly by EEG, but indirectly by measuring hemodynamic responses, using functional near-infrared spectroscopy (fNIRS), i.e., the varying concentration of oxygen in the blood in activated nerve cells in the cerebral cortex (Naseer and Hong, 2015 ).…”

Section: Reviewmentioning

confidence: 99%

“…On the contrary, Ortner et al ( 2011 ) did not find differences in reliability between experienced and naive participants and between sessions in an exogenous application ( n = 6, neurologically intact). Ten studies further assessed the temporal workload of EEG-based BCIs in the order of 2–10 s (Ortner et al, 2011 ; Webb et al, 2012 ; King et al, 2014 ; Xiao et al, 2014 ; Bi et al, 2017 ; Zhang et al, 2019 ). The fNIRS-motor execution study by Lee et al ( 2017 ) reported 78% classification accuracy and measured time from signal acquisition to movement onset as 5.84 ms ( n = 6; neurologically intact).…”

Section: Reviewmentioning

confidence: 99%

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Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

et al. 2022

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Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.

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Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

et al. 2022

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Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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Hand Exoskeleton Control for Cerebrum Plasticity Training Based on Brain–Computer Interface

Cited by 2 publications

References 10 publications

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

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