Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Baniqued, Paul Dominick E.; Stanyer, Emily C.; Awais, Muhammad; Alazmani, Ali; Jackson, Andrew; Mon‐Williams, Mark; Mushtaq, Faisal; Holt, R. Glynn

doi:10.1101/2019.12.11.19014571

Cited by 14 publications

(15 citation statements)

References 114 publications

(210 reference statements)

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“…Most of these systems were limited to triggering the device with a limited number of control commands (left or right MI tasks). However, the rehabilitation of fine motor skills and the fine control of robots are important for activities of daily living [66]. Therefore, the TD-Atten method to classify 4-joint MI from the same upper limb that we proposed could potentially be applied in fine MI-BCI systems for the control of a robotic arm or a neural prosthesis.…”

Section: Discussionmentioning

confidence: 99%

Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb

Qiu

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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A brain-computer interface (BCI) based on motor imagery (MI) from the same limb can provide an intuitive control pathway but has received limited attention. It is still a challenge to classify multiple MI tasks from the same limb. The goal of this study is to propose a novel decoding method to classify the MI tasks of four joints of the same upper limb and the resting state. EEG signals were collected from 20 participants. A time-distributed attention network (TD-Atten) was proposed to adaptively assign different weights to different classes and frequency bands of the input multiband Common Spatial Pattern (CSP) features. The long short-term memory (LSTM) and dense layers were then used to learn sequential information from the reweight features and perform the classification. Our proposed method outperformed other baseline and deep learning-based methods and obtained the accuracies of 46.8% in the 5-class scenario and 53.4% in the 4-class scenario. The visualization results of attention weights indicated that the proposed framework can adaptively pay attention to alphaband related features in MI tasks, which was consistent with the analysis of brain activation patterns. These results demonstrated the feasibility and interpretability of the attention mechanism in MI decoding and the potential of this fine MI paradigm to be applied for the control of a robotic arm or a neural prosthesis.

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Section: Discussionmentioning

confidence: 99%

Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb

Qiu

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Volitional-control robotic assistive rehabilitation improved patient treatment outcomes [16][17][18]. Most of these studies used brain wave (electroencephalography, EEG) or electromyography (EMG) signals for voluntary control of exoskeletons or end effectors [16]. In this study, ROM of UE joint was used for voluntary control of an UE exoskeleton.…”

Section: Discussionmentioning

confidence: 99%

Performance Validation of An Upper Limb Exoskeleton Using Joint ROM Signal

Alshahrani¹,

Zhou²,

Chen³

et al. 2021

Arch Orthop

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Exoskeleton systems are emerging for robotic assistive surgery and rehabilitation of neurologically impaired patients. A novel upper extremity (UE) exoskeleton has been developed in our lab, potentially to be used for robotic assistive surgery and stroke rehabilitation in our laboratory. The purpose of this study was to introduce the methodology of voluntary control of the UE exoskeleton by processing the range of motion (ROM) of UE joints. Ipsilateral-to-ipsilateral synchronous (IIS) control and ipsilateral-to-contralateral mirror (ICM) control mechanism were designed for UE exoskeleton movement control. A 3D simulation was performed to validate mechanical designs for kinesiologic motion. The performance of the ROM-controlled UE exoskeleton was then validated among six healthy subjects. The UE exoskeleton performed drawing movements in a 2D panel. The drawings created by the UE exoskeleton were compared to the drawings created by a healthy subject to determine the accuracy of the drawing performance. Reliability statistical analysis (Cronbach test) was performed to determine the inter-rater agreement between subject performance and UE exoskeleton performance. Results showed an excellent agreement between the human drawings and exoskeleton drawings (Cronbach Alpha value = 0.904, p<0.01). This study demonstrated that ROM of UE joints can be processed for voluntary control of a UE exoskeleton. Potentially, UE exoskeletons can be used for robotic assistive orthopaedic surgery and UE rehabilitation trainings.

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“…For example, EEG signals are commonly used to trigger brain–computer interface robotic devices to support movement rehabilitation following stroke. 54 Central to this approach is the need for individuals to be able self-regulate brainwave frequencies—a skill that is developed through neurofeedback training. This neurofeedback approach has also been used to optimize performance in neurologically healthy populations in a variety of tasks (see ref 55 for a review) with some work on developing surgical skills.…”

Section: Discussionmentioning

confidence: 99%

Frontal theta brain activity varies as a function of surgical experience and task error

Balkhoyor¹,

Awais

Biyani³

et al. 2020

BMJ Surg Interv Health Technologies

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ObjectiveInvestigations into surgical expertise have almost exclusively focused on overt behavioral characteristics with little consideration of the underlying neural processes. Recent advances in neuroimaging technologies, for example, wireless, wearable scalp-recorded electroencephalography (EEG), allow an insight into the neural processes governing performance. We used scalp-recorded EEG to examine whether surgical expertise and task performance could be differentiated according to an oscillatory brain activity signal known as frontal theta—a putative biomarker for cognitive control processes.Design, setting, and participantsBehavioral and EEG data were acquired from dental surgery trainees with 1 year (n=25) and 4 years of experience (n=20) while they performed low and high difficulty drilling tasks on a virtual reality surgical simulator. EEG power in the 4–7 Hz range in frontal electrodes (indexing frontal theta) was examined as a function of experience, task difficulty and error rate.ResultsFrontal theta power was greater for novices relative to experts (p=0.001), but did not vary according to task difficulty (p=0.15) and there was no Experience × Difficulty interaction (p=0.87). Brain–behavior correlations revealed a significant negative relationship between frontal theta and error in the experienced group for the difficult task (r=−0.594, p=0.0058), but no such relationship emerged for novices.ConclusionWe find frontal theta power differentiates between surgical experiences but correlates only with error rates for experienced surgeons while performing difficult tasks. These results provide a novel perspective on the relationship between expertise and surgical performance.

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Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

Cited by 14 publications

References 114 publications

Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb

Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb

Performance Validation of An Upper Limb Exoskeleton Using Joint ROM Signal

Frontal theta brain activity varies as a function of surgical experience and task error

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