Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation

Bhagat, Nikunj; Yozbatıran, Nuray; Sullivan, Jennifer L.; Paranjape, Ruta; C, Losey; Hernandez, Zachery R.; Keser, Zafer; Grossman, Robert G.; Francisco, Gerard E.; O’Malley, Marcia K.; Contreras-Vidal, José L.

doi:10.1016/j.nicl.2020.102502

Cited by 40 publications

(49 citation statements)

References 42 publications

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“…Forearm p1. Rehabilitation/Medical applications [ 81 ]

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Section: Figure A1mentioning

confidence: 99%

Sensors and Actuation Technologies in Exoskeletons: A Review

Tiboni

Borboni

Vérité

et al. 2022

Sensors

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Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.

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“…Forearm p1. Rehabilitation/Medical applications [ 81 ]

; [ 82 ]

; [ 106 ]; [ 118 ]

; [ 169 ]

; [ 100 ]; [ 170 ]

; [ 111 ]

; [ 171 ]

; [ 172 ]

; [ 99 ]

; [ 173 ]

; [ 86 ]

; [ 115 ]; [ 119 ]

; [ 25 ]; [ 73 ]; [ 103 ]

; [ 174 ]

; [ 58 ] …”

Section: Figure A1mentioning

confidence: 99%

Sensors and Actuation Technologies in Exoskeletons: A Review

Tiboni

Borboni

Vérité

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…summarizes various design considerations for upper limb exoskeleton devices [11] . These exoskeletons utilize a variety of control strategies (e.g., assistance, correction and resistance based) [12] and can also serve as an end effector for BMIs, by taking advantage of neural signals as inputs such as EMG [4] and EEG [13] , [14] .…”

Section: Overview Of State-of-the-art End Effectorsmentioning

confidence: 99%

A Roadmap Towards Standards for Neurally Controlled End Effectors

Paek

Brantley

Ravindran

et al. 2021

IEEE Open J. Eng. Med. Biol.

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The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

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“…The second one used the threedimension CNN to regress raw resting state EEG data with the FMU score, obtaining a competitive coefficient of determination (R 2 ) [24]. Motor intention can be detected from EEG during BCI training and BCI performance has been proven positively correlated with motor recovery [25][26][27]. Thus, we hypothesized that motor state EEG signals would provide solid ground for prognosis.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training

Lin

Jia

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R 2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.

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Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation

Abstract: Graphical abstract

Cited by 40 publications

References 42 publications

Sensors and Actuation Technologies in Exoskeletons: A Review

Sensors and Actuation Technologies in Exoskeletons: A Review

A Roadmap Towards Standards for Neurally Controlled End Effectors

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training

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