EEG-based neurofeedback training with shoulder exoskeleton robot assistance triggered by the contralesional primary motor cortex activity in poststroke patients with severe chronic hemiplegia

Takasaki, K.; Liu, F.; Ogura, Mariko; Okuyama, Kohei; Kawakami, Michiyuki; Mizuno, Katsuhiro; Kasuga, Shoko; Noda, Tomoyuki; Morimoto, Jun; Liu, M.; Ushiba, Junichi

doi:10.1016/j.rehab.2018.05.201

Cited by 2 publications

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“…This unit contains the Chebyshev bandpass filter (type I) which allows us to increase the signal-to-noise ratio, as well as to select the required frequency range in the EEG signal spectrum [Smetanin et al, 2018a;Smetanin et al, 2018b]. The spatial filtering unit is based on the CSP (common spatial pattern) algorithm and it is applied to separate EEG signals that correspond to different states of the subject [Takasaki et al, 2018]. For example, let X 1 and X 2 are time series (with sizes T 1 and T 2 respectively) which correspond to two states of the subject.…”

Section: Methodsmentioning

confidence: 99%

“…Depending on the size and of the impendance of the electrodes it is possible to register an activity of neuron groups or even separate neurons. These methods allow disabled people (epilepsy, Parkinson's disease, essential tremor) to control exoskeleton [Takasaki et al, 2018] or to type the text [Arvaneh et al, 2018]. However, these methods have several problems.…”

Section: Neurofeedbackmentioning

confidence: 99%

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Artificial intelligence based neurofeedback

Plotnikov

Lipkovich

Semenov

et al. 2019

CAP

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Section: Methodsmentioning

confidence: 99%

Section: Neurofeedbackmentioning

confidence: 99%

Artificial intelligence based neurofeedback

Plotnikov

Lipkovich

Semenov

et al. 2019

CAP

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“…Many robotic systems have been developed for upper limb rehabilitation. These devices can be used for wrist rehabilitation (Zhang et al, 2020;Moshaii et al, 2019;Ben Aabdallah et al, 2016) for elbow rehabilitation (Koh et al, 2017;Liu et al, 2018) and for shoulder rehabilitation (Kim et al, 2019;Takasaki et al, 2018). Also, some complex systems have been developed to cover all upper limb joints (Balasubramanian and He, 2012;Díez et al, 2016;Bouteraa and Abdallah, 2016;Senanayake and Naim, 2019;Bouteraa et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Actually, the automatic stepper motor adjustment provides a very precise customer solution. Unlike previously mentioned works that focus only on a part or a joint such as the elbow (Koh et al, 2017;Liu et al, 2018;Zhang et al, 2019), shoulder (Kim et al, 2019;Takasaki et al, 2018), wrist (Zhang et al, 2020;Moshaii et al, 2019) or hand (Bouteraa et al, 2019), the developed platform covers all the upper limb joints and their movements. Compared to similar solutions (Nef et al, 2009;Abdallah et al, 2017), the proposed system is inexpensive and lightweight.…”

Section: Introductionmentioning

confidence: 99%

Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation

Bouteraa

Abdallah

Elmogy

2020

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Purpose The purpose of this paper is to design and develop a new robotic device for the rehabilitation of the upper limbs. The authors are focusing on a new symmetrical robot which can be used to rehabilitate the right upper limb and the left upper limb. The robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The main idea is to integrate electrical stimulation into motor rehabilitation by robot. The goal is to provide automatic electrical stimulation based on muscle status during the rehabilitation process. Design/methodology/approach The developed robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The system merges two rehabilitation strategies: motor rehabilitation and electrical stimulation. The goal is to take the advantages of both approaches. Electrical stimulation is often used for building muscle through endurance, resistance and strength exercises. However, in the proposed approach the electrical stimulation is used for recovery, relaxation and pain relief. In addition, the device includes an electromyography (EMG) muscle sensor that records muscle activity in real time. The control architecture provides the ability to automatically activate the appropriate stimulation mode based on the acquired EMG signal. The system software provides two modes for stimulation activation: the manual preset mode and the EMG driven mode. The program ensures traceability and provides the ability to issue a patient status monitoring report. Findings The developed robotic device is symmetrical and reconfigurable. The presented rehabilitation system includes a muscle stimulator associated with the robot to improve the quality of the rehabilitation process. The integration of neuromuscular electrical stimulation into the physical rehabilitation process offers effective rehabilitation sessions for neuromuscular recovery of the upper limb. A laboratory-made stimulator is developed to generate three modes of stimulation: pain relief, massage and relaxation. Through the control software interface, the physiotherapist can set the exercise movement parameters, define the stimulation mode and record the patient training in real time. Research limitations/implications There are certain constraints when applying the proposed method, such as the sensitivity of the acquired EMG signals. This involves the use of professional equipment and mainly the implementation of sophisticated algorithms for signal extraction. Practical implications Functional electrical stimulation and robot-based motor rehabilitation are the most important technologies applied in post-stroke rehabilitation. The main objective of integrating robots into the rehabilitation process is to compensate for the functions lost in people with physical disabilities. The stimulation technique can be used for recovery, relaxation and drainage and pain relief. In this context, the idea is to integrate electrical stimulation into motor rehabilitation based on a robot to obtain the advantages of the two approaches to further improve the rehabilitation process. The introduction of this type of robot also makes it possible to develop new exciting assistance devices. Originality/value The proposed design is symmetrical, reconfigurable and light, covering all the joints of the upper limbs and their movements. In addition, the developed platform is inexpensive and a portable solution based on open source hardware platforms which opens the way to more extensions and developments. Electrical stimulation is often used to improve motor function and restore loss of function. However, the main objective behind the proposed stimulation in this paper is to recover after effort. The novelty of the proposed solution is to integrate the electrical stimulation powered by EMG in robotic rehabilitation.

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EEG-based neurofeedback training with shoulder exoskeleton robot assistance triggered by the contralesional primary motor cortex activity in poststroke patients with severe chronic hemiplegia

Cited by 2 publications

References 0 publications

Artificial intelligence based neurofeedback

Artificial intelligence based neurofeedback

Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation

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