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Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives

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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Systems Neuroscience".

Deadline for manuscript submissions: closed (17 November 2022) | Viewed by 30636

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Special Issue Editors

Prof. Dr. Jie Jia

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Guest Editor

Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200032, China
Interests: stroke; motor imagery; Brain Computer Interface; rehabilitation

Dr. Yan Hu

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Guest Editor

School of Nursing, Fudan University, Shanghai 200032, China
Interests: cancer nursing; evidence-based nursing practice

Dr. Dingguo Zhang

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Guest Editor

Department of Electronic and Electrical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK
Interests: human–machine interface; rehabilitation robotics; biomechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are now preparing to launch a new Special Issue of Brain Sciences on "Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives".

As we know, stroke is one of the world's leading causes of death, and the cruel aspect of stroke is that it leaves people with severe functional disability and/or cognitive impairment. Stroke has a great impact on economies worldwide, as it is estimated that about 10% of the male population and 8% of the female population are affected by it. Such people need personal help in their everyday life and must be materially supported by social services. With the advancement of medicine, artificial intelligence and new technologies have been developing rapidly and are gradually applied in diseases of the nervous system, increasingly helping diagnosis, treatment, rehabilitation and prognosis of disease.

This Special Issue aims to collect the latest researches on artificial intelligence and new technologies in stroke, including in the aspects of diagnosis, treatment, prognosis, rehabilitation and nursing.

We look forward to having your work to be a part of this collection.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Artificial intelligence in the diagnosis, treatment, rehabilitation and prognosis of
New technology in the diagnosis, treatment, rehabilitation and prognosis of stroke.

Please let us know if you have any questions. We look forward to receiving your contributions.

The journal “Brain Sciences” (ISSN 2076-3425) is indexed in *Scopus* (CiteScore = 3.0) and *SCIE*—Web of Science (IF = 3.394), with citations available in *PubMed*, full-text archived in PubMed Central. Details on: https://0-www-mdpi-com.brum.beds.ac.uk/journal/brainsci

Prof. Dr. Jie Jia
Dr. Yan Hu
Dr. Dingguo Zhang
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
innovative technique
neurological disease

Published Papers (14 papers)

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Research

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19 pages, 3116 KiB

Open AccessArticle

Application of Immersive Virtual-Reality-Based Puzzle Games in Elderly Patients with Post-Stroke Cognitive Impairment: A Pilot Study

by Zhilan Liu, Zhijie He, Jing Yuan, Hua Lin, Conghui Fu, Yan Zhang, Nian Wang, Guo Li, Jing Bu, Mei Chen and Jie Jia

Brain Sci. 2023, 13(1), 79; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13010079 - 31 Dec 2022

Cited by 8 | Viewed by 2876

Abstract

Background: The society is aging in China, and the cognitive level of elderly post-stroke patients gradually declines. Face-to-face cognitive functional training is no longer sufficient. Immersive virtual reality (IVR) is a promising rehabilitation training device. In this study, we developed an IVR-based puzzle game to explore its effectiveness, feasibility, and safety in elderly stroke patients with cognitive dysfunction. Methods: A total of 30 patients with mild post-stroke cognitive impairment after stroke were randomly assigned to a control or IVR group. Patients in both groups received routine rehabilitation therapy. Patients in the control group received traditional cognitive training, and those in the IVR group received IVR-based puzzle game therapy. Before and after treatment, Montreal cognitive assessment (MOCA), trail-making test-A (TMT-A), digit symbol substitution test (DSST), digital span test (DST), verbal fluency test (VFT), and modified Barthel index (MBI) were evaluated in both groups. In addition, the IVR group was administered a self-report questionnaire to obtain feedback on user experience. Results: There was no significant difference in the baseline data between the two groups. After six weeks of treatment, the cognitive assessment scores were improved in both groups. Moreover, the IVR group showed more improvements than the control group in the DSST (Z = 2.203, p = 0.028 < 0.05, η² = 0.16); MOCA (T = 1.186, p = 0.246 > 0.05, d = 0.44), TMT-A (T = 1.791, p = 0.084 > 0.05, d = 0.65), MBI (T = 0.783, p = 0.44 > 0.05, d = 0.28), FDST (Z = 0.78, p = 0.435 > 0.05, η² = 0.02), BDST (Z = 0.347, p = 0.728 > 0.05, η² = 0.004), and VFT(Z = 1.087, p = 0.277 > 0.05, η² = 0.039) did not significantly improve. The significant difference in DSST represents an improvement in executive function and visual–spatial cognitive characteristics. The other assessment scores did not show such features. Therefore, we did not observe significant differences through this measure. According to the results of the self-report questionnaire, most of the patients were satisfied with the equipment stability and training content. Several individuals reported mild adverse reactions. Conclusions: This pilot study suggests that IVR-based puzzle games are a promising approach to improve post-stroke cognitive function, especially executive cognitive function, and visual–spatial attention in older adults. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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13 pages, 2178 KiB

Open AccessArticle

Repetitive Transcranial Magnetic Stimulation of the Brain Region Activated by Motor Imagery Involving a Paretic Wrist and Hand for Upper-Extremity Motor Improvement in Severe Stroke: A Preliminary Study

by Tianhao Gao, Yiqian Hu, Jie Zhuang, Yulong Bai and Rongrong Lu

Brain Sci. 2023, 13(1), 69; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13010069 - 29 Dec 2022

Cited by 2 | Viewed by 1593

Abstract

Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain–computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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17 pages, 20264 KiB

Open AccessArticle

Application of Multi-Dimensional Intelligent Visual Quantitative Assessment System to Evaluate Hand Function Rehabilitation in Stroke Patients

by Yuying Du, Yu Shi, Hongmei Ma, Dong Li, Ting Su, Ou Zhabayier Meidege, Baolan Wang and Xiaofeng Lu

Brain Sci. 2022, 12(12), 1698; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12121698 - 10 Dec 2022

Cited by 2 | Viewed by 1169

Abstract

Background: Hand dysfunction is one of the main symptoms of stroke patients, but there is still a lack of accurate hand function assessment systems. This study focused on the application of the multi-dimensional intelligent visual quantitative assessment system (MDIVQAS) in the rehabilitation assessment of hand function in stroke patients and evaluate hand function rehabilitation in stroke patients. Methods: Eighty-two patients with stroke and unilateral hand dysfunction were evaluated by MDIVQAS. Cronbach’s Alpha coefficient was used to assess the internal consistency of MDIVQAS; the F-test is used to assess the differences in MDIVQAS for multiple repeated measures. Spearman’s analysis was used to identify correlations of MDIVQAS with other assessment systems. t-tests were used to identify differences in outcomes assessed with MDIVQAS in patients before and after treatment. p < 0.05 were considered significant. Results: (1) Cronbach’s Alpha coefficient of MDIVQAS in evaluating hand’s function > 0.9. (2) There was no significant difference between the other repeated measurements, except for thumb rotation in MDIVQAS. (3) MDIVQAS had a significant correlation with other assessment systems (r > 0.5, p < 0.01). (4) There were significant differences in the evaluation of hand function in patients before and after treatment using MDIVQAS. Conclusion: The MDIVQAS system has good reliability and validity in the evaluation of stroke hand function, and it can also better evaluate the treatment effect. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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20 pages, 4980 KiB

Open AccessArticle

FER-PCVT: Facial Expression Recognition with Patch-Convolutional Vision Transformer for Stroke Patients

by Yiming Fan, Hewei Wang, Xiaoyu Zhu, Xiangming Cao, Chuanjian Yi, Yao Chen, Jie Jia and Xiaofeng Lu

Brain Sci. 2022, 12(12), 1626; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12121626 - 28 Nov 2022

Cited by 3 | Viewed by 2194

Abstract

Early rehabilitation with the right intensity contributes to the physical recovery of stroke survivors. In clinical practice, physicians determine whether the training intensity is suitable for rehabilitation based on patients’ narratives, training scores, and evaluation scales, which puts tremendous pressure on medical resources. In this study, a lightweight facial expression recognition algorithm is proposed to diagnose stroke patients’ training motivations automatically. First, the properties of convolution are introduced into the Vision Transformer’s structure, allowing the model to extract both local and global features of facial expressions. Second, the pyramid-shaped feature output mode in Convolutional Neural Networks is also introduced to reduce the model’s parameters and calculation costs significantly. Moreover, a classifier that can better classify facial expressions of stroke patients is designed to improve performance further. We verified the proposed algorithm on the Real-world Affective Faces Database (RAF-DB), the Face Expression Recognition Plus Dataset (FER+), and a private dataset for stroke patients. Experiments show that the backbone network of the proposed algorithm achieves better performance than Pyramid Vision Transformer (PvT) and Convolutional Vision Transformer (CvT) with fewer parameters and Floating-point Operations Per Second (FLOPs). In addition, the algorithm reaches an 89.44% accuracy on the RAF-DB dataset, which is higher than other recent studies. In particular, it obtains an accuracy of 99.81% on the private dataset, with only 4.10M parameters. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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19 pages, 2112 KiB

Open AccessArticle

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain–Computer Interface-Based Stroke Rehabilitation

by Lei Cao, Hailiang Wu, Shugeng Chen, Yilin Dong, Changming Zhu, Jie Jia and Chunjiang Fan

Brain Sci. 2022, 12(11), 1502; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12111502 - 05 Nov 2022

Cited by 4 | Viewed by 1453

Abstract

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain–computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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18 pages, 7025 KiB

Open AccessArticle

A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study

by Yue Li, Chong Li, Xiaokang Shu, Xinjun Sheng, Jie Jia and Xiangyang Zhu

Brain Sci. 2022, 12(10), 1380; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12101380 - 12 Oct 2022

Cited by 3 | Viewed by 1678

Abstract

Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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12 pages, 1171 KiB

Open AccessArticle

The Facial Skin Blood Flow Change of Stroke Patients with Facial Paralysis after Peripheral Magnetic Stimulation: A Pilot Study

by Yongli Zhang, Shugeng Chen, Yinglu Ruan, Jiaying Lin, Chengdong Li, Chong Li, Shuo Xu, Zhijie Yan, Xiangyun Liu, Peng Miao and Jie Jia

Brain Sci. 2022, 12(10), 1271; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12101271 - 21 Sep 2022

Viewed by 1945

Abstract

Background: Facial paralysis (FP) is a common symptom after stroke, which influences the quality of life and prognosis of patients. Recently, peripheral magnetic stimulation (PMS) shows potential effects on peripheral and central nervous system damage. However, the effect of PMS on FP after stroke is still unclear. Methods: In this study, we applied PMS on the facial nerve of nine stroke patients with FP. At the same time, laser speckle contrast imaging (LSCI) was used to explore the facial skin blood flow (SkBF) in 19 healthy subjects and nine stroke patients with FP before and after the PMS intervention. The whole face was divided into 14 regions to compare the SkBF in different sub-areas. Results: In baseline SkBF, we found that there were no significant differences in the SkBF between the left and right faces in the healthy subjects. However, there was a significant difference in the SkBF between the affected and unaffected faces in Region 7 (Chin area, p = 0.046). In the following five minutes after the PMS intervention (Pre_0–5 min), the SkBF increased in Region 5 (p = 0.014) and Region 7 (p = 0.046) and there was an increasing trend in Region 3 (p = 0.088) and Region 6 (p = 0.069). In the five to ten minutes after the intervention (Post_6–10 min), the SkBF increased in Region 5 (p = 0.009), Region 6 (p = 0.021) and Region 7 (p = 0.023) and there was an increasing trend in Region 3 (p = 0.080) and left and right whole face (p = 0.051). Conclusions: These pilot results indicate that PMS intervention could increase facial skin blood flow in stroke patients with FP. A further randomized controlled trial can be performed to explore its possible clinical efficacy. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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11 pages, 547 KiB

Open AccessArticle

Effects of Repetitive Transcranial Magnetic Stimulation on Gait and Postural Control Ability of Patients with Executive Dysfunction after Stroke

by Huixian Yu, Sihao Liu, Pei Dai, Zhaoxia Wang, Changbin Liu and Hao Zhang

Brain Sci. 2022, 12(9), 1185; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12091185 - 02 Sep 2022

Cited by 4 | Viewed by 1847

Abstract

Objective: To assess the effects of repetitive transcranial magnetic stimulation (rTMS) on the gait and postural control ability of patients with executive dysfunction (ED) after stroke. Methods: A total of 18 patients with ED after stroke were randomly assigned into two groups, including an experimental group and a sham group. Patients in both groups received routine rehabilitation therapy, and patients in the experimental group underwent rTMS on the left dorsolateral prefrontal cortex (DLPFC) for 2 weeks (5 HZ, 80%MT, 1200 pulses). In the sham group, patients experienced sham stimulation treatment, in which the coil was placed vertically with the head. Before and after treatment, patients in both groups were subjected to Montreal cognitive assessment (MoCA) scoring, Fugl–Meyer assessment of lower extremity (L-FMA), Stroop color-word test (SCWT), gait analysis, foot plantar pressure test, 10-m walking test (10MWT), Berg balance scale (BBS), and timed up and go test (TUGT). In the SCWT, it was attempted to record the time of each card (SCWT-T), the correct number (SCWT-C), Stroop interference effect-time (SIE-T), and SIE correct count (SIE-C). The TUGT was categorized into four stages: getting up (GT), walking straight (WT), turning around (TT), and sitting down (ST), in which the total time of TUGT was calculated. Results: After two weeks of treatment, the evaluation indexes were improved in the two groups, some of which were statistically significant. In the experimental group, SCWT-T, SIE-T, SIE-C, GT, WT, TT, ST, and TUGT were significantly improved after treatment (p < 0.05). SCWT-C, L-FMA score, 10MWT, GT, WT, stride length, step width, foot plantar pressure, pressure center curve, and activities of daily living were not statistically different from those before treatment (p > 0.05). After treatment, SCWT-T, SIE-C, SIE-T, BBS score, TT, and ST in the experimental group were significantly shorter than those before treatment, with statistical differences (p < 0.05). Compared with the sham group, SCWT-C, L-FMA score, 10MWT, GT, WT, TUGT, stride length, step width, foot plantar pressure, pressure center curve, and motor skills were not significantly improved (p > 0.05). Conclusion: It was revealed that post-stroke rTMS treatment of patients with ED could improve executive function, improve postural control function, and reduce the risk of falling. In addition, rTMS of DLPFC could be a therapeutic target for improving postural control ability and reducing the risk of falling. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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15 pages, 4500 KiB

Open AccessArticle

Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study

by Zhijie He, Rongrong Lu, Yihui Guan, Yi Wu, Jingjie Ge, Gang Liu, Ying Chen, Hongyu Xie, Junfa Wu and Jie Jia

Brain Sci. 2022, 12(7), 892; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070892 - 07 Jul 2022

Cited by 3 | Viewed by 1927

Abstract

Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone ¹⁸F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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11 pages, 1450 KiB

Open AccessArticle

Effects of Electromyography Bridge on Upper Limb Motor Functions in Stroke Participants: An Exploratory Randomized Controlled Trial

by Qin Zhao, Gongwei Jia, Lang Jia, Yule Wang, Wei Jiang, Yali Feng, Hang Jiang, Lehua Yu, Jing Yu and Botao Tan

Brain Sci. 2022, 12(7), 870; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070870 - 30 Jun 2022

Cited by 1 | Viewed by 1386

Abstract

The electromyography bridge (EMGB) plays an important role in promoting the recovery of wrist joint function in stroke patients. We investigated the effects of the EMGB on promoting the recovery of upper limb function in hemiplegia. Twenty-four stroke patients with wrist dorsal extension dysfunction were recruited. Participants were randomized to undergo EMGB treatment or neuromuscular electrical stimulation (NMES). Treatments to wrist extensors were conducted for 25 min, twice a day, 5 days per week, for 1 month. Outcome measures: active range of motion (AROM) of wrist dorsal extension; Fugl-Meyer assessment for upper extremity (FMA-UE); Barthel index (BI); and muscle strength of wrist extensors. After interventions, patients in the NMES group had significantly greater improvement in the AROM of wrist dorsal extension at the 4th week and 1st month follow-up (p < 0.05). However, patients in the EMGB group had a statistically significant increase in AROM only at the follow-up assessment. No significant differences were observed in the AROM between the EMGB group and the NMES group (p > 0.05). For secondary outcomes in the EMGB group, compared to baseline measurements, FMA-UE, BI, extensor carpi radialis and extensor carpi ulnaris muscle strength were significantly different as early as the 4th week (p < 0.05). The muscle strength of the extensor digitorum communis muscle showed significant differences at the follow-up (p < 0.05). There were no statistically significant differences between patients in the two groups in any of the parameters evaluated (p > 0.05). The combination of EMGB or NMES with conventional treatment had similar effects on the improvement of the hemiplegic upper limb as assessed by wrist dorsal extension, FMA-UE, and activities of daily living. The improvement in both groups was maintained until 1 month after the intervention. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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Review

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14 pages, 260 KiB

Open AccessReview

A Review of Hand Function Rehabilitation Systems Based on Hand Motion Recognition Devices and Artificial Intelligence

by Yuexing Gu, Yuanjing Xu, Yuling Shen, Hanyu Huang, Tongyou Liu, Lei Jin, Hang Ren and Jinwu Wang

Brain Sci. 2022, 12(8), 1079; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12081079 - 15 Aug 2022

Cited by 9 | Viewed by 3609

Abstract

The incidence of stroke and the burden on health care and society are expected to increase significantly in the coming years, due to the increasing aging of the population. Various sensory, motor, cognitive and psychological disorders may remain in the patient after survival from a stroke. In hemiplegic patients with movement disorders, the impairment of upper limb function, especially hand function, dramatically limits the ability of patients to perform activities of daily living (ADL). Therefore, one of the essential goals of post-stroke rehabilitation is to restore hand function. The recovery of motor function is achieved chiefly through compensatory strategies, such as hand rehabilitation robots, which have been available since the end of the last century. This paper reviews the current research status of hand function rehabilitation devices based on various types of hand motion recognition technologies and analyzes their advantages and disadvantages, reviews the application of artificial intelligence in hand rehabilitation robots, and summarizes the current research limitations and discusses future research directions. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

Other

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12 pages, 299 KiB

Open AccessSystematic Review

Sensorimotor Rhythm-Based Brain–Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review

by Jianghong Fu, Shugeng Chen and Jie Jia

Brain Sci. 2023, 13(1), 56; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13010056 - 28 Dec 2022

Cited by 7 | Viewed by 2467

Abstract

Brain–computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients’ difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

12 pages, 1200 KiB

Open AccessCase Report

Application of a Brain–Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report

by Wen Gao, Zhengzhe Cui, Yang Yu, Jing Mao, Jun Xu, Leilei Ji, Xiuli Kan, Xianshan Shen, Xueming Li, Shiqiang Zhu and Yongfeng Hong

Brain Sci. 2022, 12(8), 1083; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12081083 - 16 Aug 2022

Cited by 4 | Viewed by 1906

Abstract

(1) Objective: To investigate the feasibility, safety, and effectiveness of a brain–computer interface (BCI) system with visual and motor feedback in limb and brain function rehabilitation after stroke. (2) Methods: First, we recruited three hemiplegic stroke patients to perform rehabilitation training using a BCI system with visual and motor feedback for two consecutive days (four sessions) to verify the feasibility and safety of the system. Then, we recruited five other hemiplegic stroke patients for rehabilitation training (6 days a week, lasting for 12–14 days) using the same BCI system to verify the effectiveness. The mean and Cohen’s w were used to compare the changes in limb motor and brain functions before and after training. (3) Results: In the feasibility verification, the continuous motor state switching time (CMSST) of the three patients was 17.8 ± 21.0s, and the motor state percentages (MSPs) in the upper and lower limb training were 52.6 ± 25.7% and 72.4 ± 24.0%, respectively. The effective training revolutions (ETRs) per minute were 25.8 ± 13.0 for upper limb and 24.8 ± 6.4 for lower limb. There were no adverse events during the training process. Compared with the baseline, the motor function indices of the five patients were improved, including sitting balance ability, upper limb Fugel–Meyer assessment (FMA), lower limb FMA, 6 min walking distance, modified Barthel index, and root mean square (RMS) value of triceps surae, which increased by 0.4, 8.0, 5.4, 11.4, 7.0, and 0.9, respectively, and all had large effect sizes (Cohen’s w ≥ 0.5). The brain function indices of the five patients, including the amplitudes of the motor evoked potentials (MEP) on the non-lesion side and lesion side, increased by 3.6 and 3.7, respectively; the latency of MEP on the non-lesion side was shortened by 2.6 ms, and all had large effect sizes (Cohen’s w ≥ 0.5). (4) Conclusions: The BCI system with visual and motor feedback is applicable in active rehabilitation training of stroke patients with hemiplegia, and the pilot results show potential multidimensional benefits after a short course of treatment. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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14 pages, 1817 KiB

Open AccessSystematic Review

Effects of Non-Invasive Brain Stimulation on Post-Stroke Spasticity: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

by Xiaohan Wang, Le Ge, Huijing Hu, Li Yan and Le Li

Brain Sci. 2022, 12(7), 836; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12070836 - 27 Jun 2022

Cited by 9 | Viewed by 2743

Abstract

In recent years, the potential of non-invasive brain stimulation (NIBS) for the therapeutic effect of post-stroke spasticity has been explored. There are various NIBS methods depending on the stimulation modality, site and parameters. The purpose of this study is to evaluate the efficacy of NIBS on spasticity in patients after stroke. This systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. PUBMED (MEDLINE), Web of Science, Cochrane Library and Excerpta Medica Database (EMBASE) were searched for all randomized controlled trials (RCTs) published before December 2021. Two independent researchers screened relevant articles and extracted data. This meta-analysis included 14 articles, and all included articles included 18 RCT datasets. The results showed that repetitive transcranial magnetic stimulation (rTMS) (MD = −0.40, [95% CI]: −0.56 to −0.25, p < 0.01) had a significant effect on improving spasticity, in which low-frequency rTMS (LF-rTMS) (MD = −0.51, [95% CI]: −0.78 to −0.24, p < 0.01) and stimulation of the unaffected hemisphere (MD = −0.58, [95% CI]: −0.80 to −0.36, p < 0.01) were beneficial on Modified Ashworth Scale (MAS) in patients with post-stroke spasticity. Transcranial direct current stimulation (tDCS) (MD = −0.65, [95% CI]: −1.07 to −0.22, p < 0.01) also had a significant impact on post-stroke rehabilitation, with anodal stimulation (MD = −0.74, [95% CI]: −1.35 to −0.13, p < 0.05) being more effective in improving spasticity in patients. This meta-analysis revealed moderate evidence that NIBS reduces spasticity after stroke and may promote recovery in stroke survivors. Future studies investigating the mechanisms of NIBS in addressing spasticity are warranted to further support the clinical application of NIBS in post-stroke spasticity. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence in Stroke from the Clinical, Rehabilitation and Nursing Perspectives)

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