Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics

Hayashibe, Mitsuhiro; Guiraud, David; Pons, José L; Farina, Dario

doi:10.3389/fnins.2015.00434

Cited by 10 publications

(6 citation statements)

References 26 publications

(28 reference statements)

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“…The development of a tridirectional (user-decoder-robot) BCI control of multi-DOF control is a novel attempt toward its aim. The results obtained in this preliminary study demonstrate that the use of automated interfaces to solve redundancy of joint movement is indeed possible and the positive results encourage us to further dwell into developing a tridirectional adaptive system for rehabilitative and assistive systems [58], [59].…”

Section: Discussionmentioning

confidence: 62%

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Bhattacharyya

Shimoda²,

Hayashibe

2016

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes a novel brain-machine interfacing (BMI) paradigm for control of a multijoint redundant robot system. Here, the user would determine the direction of end-point movement of a 3-degrees of freedom (DOF) robot arm using motor imagery electroencephalography signal with coadaptive decoder (adaptivity between the user and the decoder) while a synergetic motor learning algorithm manages a peripheral redundancy in multi-DOF joints toward energy optimality through tacit learning. As in human motor control, torque control paradigm is employed for a robot to be adaptive to the given physical environment. The dynamic condition of the robot arm is taken into consideration by the learning algorithm. Thus, the user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI. The support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user. Online experiments reveals that the users successfully reach their targets with an average decoder accuracy of over 75% in different end-point load conditions. Index Terms-Brain-machine interfacing (BMI), co-adaptive decoder, joint redundancy, multijoint robot, synergetic learning control, tacit learning. I. INTRODUCTION A S OF today, brain-machine interfacing (BMI) [or braincomputer interfacing (BCI)] is one of the fastest growing areas of research that provides a unique course of communication between a human and a machine (or device) without any neuro-muscular intervention [1]. BMI was initially conceived to provide rehabilitative and assistive solutions [2], [3] to patients suffering from neuromuscular degenerative diseases, such as amyotropic lateral sclerosis, cervical spinal injury, paralysis, or amputee [4].

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Section: Discussionmentioning

confidence: 62%

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Bhattacharyya

Shimoda²,

Hayashibe

2016

IEEE Trans. Syst. Man Cybern, Syst.

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“…Most closed-loop FES systems addressed in previous works were established between the electrical stimulus and joint angle since it is more convenient to measure and process the joint angle than joint torque or muscle force. However, in order to take the immediate effect of muscle activity induced by FES control into account, introducing biofeedback into closed-loops should be considered for further FES development (Bruns et al, 2013 ; Hayashibe et al, 2015 ), thus contributing to individualized modeling and control to manage different muscle responses from each different subject.…”

Section: Personalized Electrical Stimulation Through Evoked Emgmentioning

confidence: 99%

Evoked Electromyographically Controlled Electrical Stimulation

Hayashibe

2016

Front. Neurosci.

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Time-variant muscle responses under electrical stimulation (ES) are often problematic for all the applications of neuroprosthetic muscle control. This situation limits the range of ES usage in relevant areas, mainly due to muscle fatigue and also to changes in stimulation electrode contact conditions, especially in transcutaneous ES. Surface electrodes are still the most widely used in noninvasive applications. Electrical field variations caused by changes in the stimulation contact condition markedly affect the resulting total muscle activation levels. Fatigue phenomena under functional electrical stimulation (FES) are also well known source of time-varying characteristics coming from muscle response under ES. Therefore, it is essential to monitor the actual muscle state and assess the expected muscle response by ES so as to improve the current ES system in favor of adaptive muscle-response-aware FES control. To deal with this issue, we have been studying a novel control technique using evoked electromyography (eEMG) signals to compensate for these muscle time-variances under ES for stable neuroprosthetic muscle control. In this perspective article, I overview the background of this topic and highlight important points to be aware of when using ES to induce the desired muscle activation regardless of the time-variance. I also demonstrate how to deal with the common critical problem of ES to move toward robust neuroprosthetic muscle control with the Evoked Electromyographically Controlled Electrical Stimulation paradigm.

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“…However, in people with SCI, the application of NMES accelerates the process of neuromuscular fatigue due to the influence of the increased proportion of type II fibers and impacts muscle response time . Different time patterns and the amplitude of the NMES pulse have been reported in the literature with the purpose of delaying the muscle fatigue process, aiming for better efficiency of NMES in hybrid neuroprostheses …”

Section: Introductionmentioning

confidence: 99%

“…15 Different time patterns and the amplitude of the NMES pulse have been reported in the literature with the purpose of delaying the muscle fatigue process, aiming for better efficiency of NMES in hybrid neuroprostheses. 16,17 The time lag between the onset of the muscle electrical activation and force production is called the electrical mechanical delay (EMD). [18][19][20] Electrochemical (synaptic transmission, excitation-contraction coupling process) and mechanical (power transmission over the elastic components) events occur during EMD, essentially influenced by the types of muscle fiber 5,[21][22][23] and by the fatigue process.…”

Section: Introductionmentioning

confidence: 99%

Fatigue in complete spinal cord injury and implications on total delay

et al. 2019

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The use of neuromuscular electrical stimulation (NMES) to artificially restore movement in people with complete spinal cord injury (SCI) induces an accelerated process of muscle fatigue. Fatigue increases the time between the beginning of NMES and the onset of muscle force (DelayTOT). Understanding how much muscle fatigue affects the DelayTOT in people with SCI could help in the design of closed‐loop neuroprostheses that compensate for this delay, thus making the control system more stable. The aim of this study was to evaluate the impact of the extent of fatigue on DelayTOT and peak force of the lower limbs in people with complete SCI. Fifteen men—young adults with complete SCI (paraplegia and tetraplegia) and stable health—participated in the experiment. DelayTOT was defined as the time interval between the beginning of NMES application until the onset of muscle force. The electrical intensity of NMES applied was adjusted individually and consisted of the amplitude required to obtain a full extension of the knee (0°), considering the maximum electrically stimulated extension (MESE). Subsequently, 70% of the MESE was applied during the fatigue induction protocol. Significant differences were identified between the moments before and after the fatigue protocol, both for peak force (P ≤ .026) and DelayTOT (P ≤ .001). The medians and interquartile range of the DelayTOT were higher in postfatigue (199.0 ms) when compared to the moment before fatigue (146.5 ms). The medians and interquartile range of the peak force were higher in unfatigued lower limbs (0.43 kgf) when compared to the moment postfatigue (0.27 kgf). The results support the hypothesis that muscle fatigue influences the increase in DelayTOT and decrease in force production in people with SCI. For future applications, the combined evaluation of the delay and force in SCI patients provides valuable feedback for NMES paradigms. The study will provide potentially critical muscle mechanical evidence for the investigation of the evolution of atrophy.

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Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics

Cited by 10 publications

References 26 publications

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Evoked Electromyographically Controlled Electrical Stimulation

Fatigue in complete spinal cord injury and implications on total delay

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