EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review

Al-Quraishi, Maged S.; Elamvazuthi, Irraivan; Daud, Siti Asmah; Parasuraman, S.; Borboni, Alberto

doi:10.3390/s18103342

Cited by 109 publications

(60 citation statements)

References 86 publications

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“…The classification accuracies for active and passive walking with baseline were 94 and 93%, respectively, demonstrating the capability of BCI-assisted robotic training. The majority of EEGbased control in lower limb studies (N = 11; 79%) included in this cited review (Al-Quraishi et al, 2018) were markedly published from 2015 onwards, indicating a relatively new research area and, in part, explaining the poor penetration in the stroke population identified in this current systematic review. Another review of brain-machine interfaces for controlling lower limb powered robotic systems (He et al, 2018a) identifies that the most common studies in this area are classification-based studies of walk vs. stand tasks in healthy subjects and system performance is not clearly presented in these studies.…”

Section: Discussionmentioning

confidence: 89%

“…While this is informative with respect to the current state of the art in this area in stroke rehabilitation, it does not reflect the broader field of EEG-based control for robotic gait devices well. A recent systematic review by Al-Quraishi et al ( 2018 ) comprehensively reported on EEG-based control for upper and lower limb exoskeletons and prostheses. In this review, 14 studies that used EEG-based control for lower limb movement, primarily in healthy subjects and individuals with spinal cord injury, were identified.…”

Section: Discussionmentioning

confidence: 99%

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A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

et al. 2020

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Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Lennon et al. Stroke: Neural Interfaced Robotic Walking Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

et al. 2020

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“…In [9]- [13], brain-computer interfaces (BCIs) for exoskeletons were implemented by measuring and decoding the wearer's EEG signals. The decoding process includes a model that is trained offline to fit neural signals to actual movements; therefore, it is time consuming to adjust the model for each individual user [3], and the reliability of the model is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

Liang

Hsiao

2020

IEEE Access

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In recent years, exoskeletons have been widely accepted as rehabilitative and walking assistive devices for either healthy persons or patients with mobility impairment. Depending on mobility of the wearers, the control strategies for exoskeletons may differ significantly. This paper aims at developing an admittance control framework for exoskeletons to assist healthy persons in safer, faster, or more energyefficient walking. A crucial step to accomplish assistive walking for healthy persons is to detect their intention. Direct sensing of the wearer's biological signals, such as electromyography (EMG) and electroencephalography (EEG), requires additional sensors, which increases the cost and makes it inconvenient for the wearer to put on and take off the exoskeleton. Instead, we propose to detect the wearer's intention by estimating the total torques applied from the wearer to the human-exoskeleton system based on the motor current and joint angles. Then the velocity commands to the joint motors are generated according to the estimated torque and a predefined mechanical admittance function of each joint. Consequently, the exoskeleton complies with the wearer's intention. Rigorous theoretical analysis is performed on robust stability of the closed-loop system. Then experiments are carried out to verify the accuracy and robustness of the proposed torque estimation algorithm. In addition, experimental data show that the wearer's gait can be shaped in a desired way by choosing an appropriate admittance function in the proposed admittance control loop.

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“…Current advances in bio-signal sensors, data acquisition, embedded systems and processing techniques contribute to the integration of physiological signals in a wide variety of clinical and non-clinical settings like medical diagnosis [1], biorobotics [2], brain-computer interfaces (BCI) or brainmachine interfaces (BMI) [3], biometrics [4]. These biosignals use different modalities: the electrocardiography (ECG), the electromyography (EMG), the electrooculography (EOG), the electrocorticography (ECoG), the electroencephalography (EEG), the positron emission tomography (PET), the magnetic resonance imaging (MRI), the functional MRI (fMRI), and the diffusion tensor imaging (DTI).…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the EEG-based control methods involve also a lot of attention in the bio-robotic applications. These methods decode the user's brain signals to control robots such as prosthetics [14,15], exoskeletons, orthoses [16][17][18] and wheelchairs [19,20]. Unfortunately, the use of an isolated EEG signal is not fully recognized in the bio-robotic domain because of the low reliability and data transfer rate [5].…”

Section: Introductionmentioning

confidence: 99%

WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification

Sbargoud¹,

Djeha²,

Guiatni³

et al. 2019

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The electromyography (EMG) and electroencephalography (EEG) are two frequently used modalities of bio-signals in the field of bio-robotics. However, it is insufficient to use a single isolated modality, due to the presence of artifacts, the lack of information, etc. To solve the problem, this paper proposes an EEG/EMG data fusion method that take advantages of both signals and overcome their drawbacks to achieve accurate identification of movements. The two types of bio-signals were preprocessed through wavelet packets transform (WPT) and classified by the artificial neural network (ANN). Then, the belief theory was introduced to allow for model uncertainty and imprecision, which adapts to the ambiguities and conflicts between sources. Experimental results show that the proposed EEG/EMG data fusion method outperformed the strategies based on only one modality. The research findings provide new impetus to bio-robotic applications.

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EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review

Cited by 109 publications

References 86 publications

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification

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