Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation

Benzy, V.K.; Vinod, A. P.; Ramakrishnan, Sivasubramanian; Alladi, Suvarna; Kenchaiah, Raghavendra

doi:10.1109/tnsre.2020.3039331

Cited by 55 publications

(33 citation statements)

References 54 publications

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“…The 10 s triggering time window in the real-time control was comparable to the BCI-MI systems for stroke rehabilitation [10], e.g. 6 s [13] and 7 s [48] for motor imagery, although it was longer than the EMG-driven system, e.g. 100 ms [49].…”

Section: Feasibility Of the Cmc-emg-driven Controlmentioning

confidence: 99%

Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke

Guo

Zhou

et al. 2022

J. Neural Eng.

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Objective: The central-to-peripheral voluntary motor effort (VME) in physical practice of the paretic limb is a dominant force for driving functional neuroplasticity on motor restoration post-stroke. However, current rehabilitation robots isolated the central and peripheral involvements in the control design, resulting in limited rehabilitation effectiveness. The purpose of this study was to design a corticomuscular coherence (CMC) and electromyography (EMG)-driven (CMC-EMG-driven) system with central-and-peripheral integrated representation of VME for wrist-hand rehabilitation after stroke. Approach: The CMC-EMG-driven control was developed in a neuromuscular electrical stimulation (NMES)-robot system, i.e., CMC-EMG-driven NMES-robot system, to instruct and assist the wrist-hand extension and flexion in persons after stroke. A pilot single-group trial of 20 training sessions was conducted with the developed system to assess the feasibility for wrist-hand practice on the chronic strokes (n=16). The rehabilitation effectiveness was evaluated through clinical assessments, CMC, and EMG activation levels. Main results: The trigger success rate and laterality index (LI) of CMC were significantly increased in wrist-hand extension across training sessions (p<0.05). After the training, significant improvements in the target wrist-hand joints and suppressed compensation from the proximal shoulder-elbow joints were observed through the clinical scores and EMG activation levels (p<0.05). The central-to-peripheral VME distribution across upper extremity (UE) muscles was also significantly improved, as revealed by the CMC values (p<0.05). Significance: Precise wrist-hand rehabilitation was achieved by the developed system, presenting suppressed cortical and muscular compensation from the contralesional hemisphere and the proximal UE, and improved distribution of the central-and-peripheral VME on UE muscles.

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Section: Feasibility Of the Cmc-emg-driven Controlmentioning

confidence: 99%

Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke

Guo

Zhou

et al. 2022

J. Neural Eng.

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“…We consider that implementing a multi-class classification paradigm that includes a class related to non-movement state is important since two-class models employed in BCI systems are generally trained to discriminate between two highly related and mutually exclusive conditions (stop -no stop or antagonistic tasks) [68], [70], [71]. In the case of BCI systems which discriminate intentions of different limb movements, these motor imagery conditions are not mutually exclusive.…”

Section: Discussionmentioning

confidence: 99%

Brain-Computer Interface Controlled Functional Electrical Stimulation: Evaluation With Healthy Subjects and Spinal Cord Injury Patients

et al. 2022

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“…For the analysis of brain electrophysiological data, previous studies have shown that patients with stroke can elicit ERD/ERS during imagining the affected limb movement (Pfurtscheller and Aranibar, 1979;Neuper et al, 2006). Benzy et al (2020) analyzed ERD/ERS and took phase locking value (PLV) as a feature to decode the direction of MI of the affected hand of patients with stroke. In this article, ERD and ERS are used to characterize the change of feature level induced by MI with the process of training.…”

Section: Discussionmentioning

confidence: 99%

Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke

et al. 2022

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Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-based brain computer interface (MI-BCI) combined with functional electrical stimulation (FES) is a potential active neural rehabilitation technology for patients with stroke recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI-based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multimodal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory, and tactile multisensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after 4 weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns, and cerebral oxygen response of patients with stroke were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation.

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Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation

Cited by 55 publications

References 54 publications

Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke

Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke

Brain-Computer Interface Controlled Functional Electrical Stimulation: Evaluation With Healthy Subjects and Spinal Cord Injury Patients

Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke

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