Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients

Lu, Ruqian; Zheng, Mou‐Xiong; Li, Jie; Gao, Tianhao; Hua, Xu‐Yun; Liu, Gang; Huang, Songhua; Xu, Jian-Guang; Wu, Yi

doi:10.1016/j.neulet.2019.134727

Cited by 33 publications

(19 citation statements)

References 38 publications

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“…A previous study reported that PM (i.e., the execution of a movement by an external agency without voluntary control) and MI induce similar EEG patterns over the motor cortex (Arvaneh et al, 2017). Many previous studies have tried using MI-based BCI control with passive motion for stroke patients, and significant positive results have been confirmed (Arvaneh et al, 2017;Cantillo-Negrete et al, 2018;Lu et al, 2020). Because a tangible object can be applied to a hand or foot when the patient performs a passive movement using an external agent, the proposed method may have a positive effect on stroke patients.…”

Section: Discussionmentioning

confidence: 95%

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

Park

Kim

et al. 2021

Front. Neurosci.

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The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.e., hard and rough balls), with a focus on poorly performing users. The proposed method is a hybrid of training and imagery, combining motor execution and somatosensory sensation from a ball-type stimulus. Fourteen healthy participants participated in the somatosensory-motor imagery (SMI) experiments (within-subject design) involving EEG data classification with a three-class system (signaling with left hand, right hand, or right foot). In the scenario of controlling a remote robot to move it to the target point, the participants performed MI when faced with a three-way intersection. The SMI condition had a better classification performance than did the MI condition, achieving a 68.88% classification performance averaged over all participants, which was 6.59% larger than that in the MI condition (p < 0.05). In poor performers, the classification performance in SMI was 10.73% larger than in the MI condition (62.18% vs. 51.45%). However, good performers showed a slight performance decrement (0.86%) in the SMI condition compared to the MI condition (80.93% vs. 81.79%). Combining the brain signals from the motor and somatosensory cortex, the proposed hybrid MI-BCI system demonstrated improved classification performance, this phenomenon was predominant in poor performers (eight out of nine subjects). Hybrid MI-BCI systems may significantly contribute to reducing the proportion of BCI-inefficiency users and closing the performance gap with other BCI systems.

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Section: Discussionmentioning

confidence: 95%

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

Park

Kim

et al. 2021

Front. Neurosci.

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“…Two articles each used Advanced Brain Monitoring [ 253 , 254 ] and Nihon Kohden [ 255 , 256 ] equipment. One article each used the equipment of Mega Electronic [ 257 ], Jordan NeuroScience Inc. [ 258 ], BIOPAC Systems Inc. [ 259 ], Laxtha Inc. [ 260 ], TMSi [ 261 ], NeuroBioLab [ 262 ], Cognionics Inc. [ 263 ], Jingahi [ 264 ], VIASYS Healthcare [ 265 ], ANT Neuro [ 266 ], Wearable Sensing [ 267 ], Netech [ 268 ], MindMedia [ 269 ], NCC Medical Co. [ 270 ], and Electrical Geodesics Inc. [ 271 ].…”

Section: Resultsmentioning

confidence: 99%

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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“…In our previous study [ 34 ], we combined wrist CPM with MI-based BCI, finding that chronic stroke patients with severe upper limb motor impairment might improve paretic wrist ROM after the intervention. This might indicate that they could benefit from this therapy.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have confirmed its clinical efficacy, and the improvement has a certain degree of continuity [ 33 ]. In our previous study [ 34 ], continuous passive motion (CPM) was used as an external device to improve wrist extension in chronic stroke patients. The results showed that after six weeks of interventions, 81% of the patients who completed the study had regained wrist extension.…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery-Based Brain-Computer Interface Combined with Multimodal Feedback to Promote Upper Limb Motor Function after Stroke: A Preliminary Study

Gao

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Background. Recently, the brain-computer interface (BCI) has seen rapid development, which may promote the recovery of motor function in chronic stroke patients. Methods. Twelve stroke patients with severe upper limb and hand motor impairment were enrolled and randomly assigned into two groups: motor imagery (MI)-based BCI training with multimodal feedback (BCI group, n = 7) and classical motor imagery training (control group, n = 5). Motor function and electrophysiology were evaluated before and after the intervention. The Fugl-Meyer assessment-upper extremity (FMA-UE) is the primary outcome measure. Secondary outcome measures include an increase in wrist active extension or surface electromyography (the amplitude and cocontraction of extensor carpi radialis during movement), the action research arm test (ARAT), the motor status scale (MSS), and Barthel index (BI). Time-frequency analysis and power spectral analysis were used to reflect the electroencephalogram (EEG) change before and after the intervention. Results. Compared with the baseline, the FMA-UE score increased significantly in the BCI group ( p = 0.006). MSS scores improved significantly in both groups, while ARAT did not improve significantly. In addition, before the intervention, all patients could not actively extend their wrists or just had muscle contractions. After the intervention, four patients regained the ability to extend their paretic wrists (two in each group). The amplitude and area under the curve of extensor carpi radialis improved to some extent, but there was no statistical significance between the groups. Conclusion. MI-based BCI combined with sensory and visual feedback might improve severe upper limb and hand impairment in chronic stroke patients, showing the potential for application in rehabilitation medicine.

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Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients

Cited by 33 publications

References 38 publications

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Motor Imagery-Based Brain-Computer Interface Combined with Multimodal Feedback to Promote Upper Limb Motor Function after Stroke: A Preliminary Study

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