Combination of Brain-Computer Interface Training and Goal-Directed Physical Therapy in Chronic Stroke: A Case Report

Background. There is no accepted and efficient rehabilitation strategy to reduce focal impairments for patients with chronic stroke who lack residual movements. Methods . A 67-year-old hemiplegic patient with no active finger extension was trained with a brain—computer interface (BCI) combined with a specific daily life—oriented physiotherapy. The BCI used electrical brain activity (EEG) and magnetic brain activity (MEG) to drive an orthosis and a robot affixed to the patient’s affected upper extremity, which enabled him to move the paralyzed arm and hand driven by voluntary modulation of μ-rhythm activity. In addition, the patient practiced goal-directed physiotherapy training. Over 1 year, he completed 3 training blocks. Arm motor function, gait capacities (using Fugl-Meyer Assessment, Wolf Motor Function Test, Modified Ashworth Scale, 10-m walk speed, and goal attainment score), and brain reorganization (functional MRI, MEG) were repeatedly assessed. Results. The ability of hand and arm movements as well as speed and safety of gait improved significantly (mean 46.6%). Improvement of motor function was associated with increased μ-oscillations in the ipsilesional motor cortex. Conclusion. This proof-of-principle study suggests that the combination of BCI training with goal-directed, active physical therapy may improve the motor abilities of chronic stroke patients despite apparent initial paralysis.

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Combined action observation- and motor imagery-based brain computer interface (BCI) for stroke rehabilitation: a case report

10.21203/rs.3.rs-610878/v1 ◽

2021 ◽

Author(s):

Nuttawat Rungsirisilp ◽

Yodchanan Wongsawat

Keyword(s):

Physical Therapy ◽

Upper Extremity ◽

Motor Function ◽

Motor Imagery ◽

Stroke Rehabilitation ◽

Brain Activity ◽

Brain Computer Interface ◽

Action Observation ◽

Activity Patterns ◽

Computer Interface

Abstract Introduction: Upper extremity impairment is a problem usually found in poststroke patients, and it is seldom completely improved even following conventional physical therapy. Motor imagery (MI) and action observation (AO) therapy are mental practices that may regain motor function in poststroke patients, especially when integrating them with brain-computer interface (BCI) technology. However, previous studies have always investigated the effects of an MI- or AO-based BCI for stroke rehabilitation separately. Therefore, in this study, we aimed to propose the effectiveness of a combined AO and MI (AOMI)-based BCI with functional electrical stimulation (FES) feedback to improve upper limb functions and alter brain activity patterns in chronic stroke patients.Case presentation: A 53-year-old male who was 12 years post stroke was left hemiparesis and unable to produce any wrist and finger extension.Intervention: The participant was given an AOMI-based BCI with FES feedback 3 sessions per week for 4 consecutive weeks, and he did not receive any conventional physical therapy during the intervention. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) and active range of motion (AROM) of wrist extension were used as clinical assessments, and the laterality coefficient (LC) value was applied to explore the altered brain activity patterns affected by the intervention.Outcomes: The FMA-UE score improved from 34 to 46 points, and the AROM of wrist extension was increased from 0 degrees to 20 degrees. LC values in the alpha band tended to be positive whereas LC values in the beta band seemed to be slightly negative after the intervention.Conclusion: An AOMI-based BCI with FES feedback training may be a promising strategy that could improve motor function in poststroke patients; however, its efficacy should be studied in a larger population and compared to that of other therapeutic methods.Trial registration: Thai Clinical Trial Registry: TCTR20200821002. Registered 17 August 2020, http://www.thaiclinicaltrials.org

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Sources of Electrical Brain Activity Most Relevant to Performance of Brain-Computer Interface Based on Motor Imagery

Brain-Computer Interface Systems - Recent Progress and Future Prospects ◽

10.5772/55166 ◽

2013 ◽

Cited By ~ 5

Author(s):

Alexander Frolov ◽

Duan Hsek ◽

Pavel Bobrov ◽

Olesya Mokienko ◽

Jaroslav Tinter

Keyword(s):

Motor Imagery ◽

Brain Activity ◽

Brain Computer Interface ◽

Computer Interface ◽

Electrical Brain Activity

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Brain–Computer Interface Based on Motor Imagery: The Most Relevant Sources of Electrical Brain Activity

Advances in Intelligent Systems and Computing - Soft Computing in Industrial Applications ◽

10.1007/978-3-319-00930-8_14 ◽

2013 ◽

pp. 153-163 ◽

Cited By ~ 1

Author(s):

Alexander A. Frolov ◽

Dušan Húsek ◽

Václav Snášel ◽

Pavel Bobrov ◽

Olesya Mokienko ◽

...

Keyword(s):

Motor Imagery ◽

Brain Activity ◽

Brain Computer Interface ◽

Computer Interface ◽

Electrical Brain Activity

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

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/1116126 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yi-Qian Hu ◽

Tian-Hao Gao ◽

Jie Li ◽

Jia-Chao Tao ◽

Yu-Long Bai ◽

...

Keyword(s):

Motor Function ◽

Motor Imagery ◽

Upper Limb ◽

Brain Computer Interface ◽

Chronic Stroke ◽

Computer Interface ◽

Stroke Patients ◽

Multimodal Feedback ◽

Before And After ◽

Extensor Carpi Radialis

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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Effortless Retaliation: the Neural Dynamics of Interpersonal Intentions in a Chicken Game Using Brain-Computer Interface

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsab064 ◽

2021 ◽

Author(s):

Yiwen Wang ◽

Yuxiao Lin ◽

Chao Fu ◽

Zhihua Huang ◽

Rongjun Yu ◽

...

Keyword(s):

Brain Activity ◽

Brain Computer Interface ◽

Neural Mechanisms ◽

Computer Interface ◽

Neural Dynamics ◽

Neural Signals ◽

Chicken Game ◽

Common Response ◽

Tit For Tat ◽

Novel Method

Abstract The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by low-level response related brain activity. Using an EEG-based brain-computer interface (BCI) combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.

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2190 Longitudinal changes in EEG power envelope connectivity are proportional to motor recovery in chronic stroke patients

Journal of Clinical and Translational Science ◽

10.1017/cts.2018.92 ◽

2018 ◽

Vol 2 (S1) ◽

pp. 17-17

Author(s):

Joseph B. Humphries ◽

David T. Bundy ◽

Eric C. Leuthardt ◽

Thy N. Huskey

Keyword(s):

Motor Cortex ◽

Motor Imagery ◽

Stroke Rehabilitation ◽

Brain Computer Interface ◽

Chronic Stroke ◽

Motor Recovery ◽

Stroke Recovery ◽

Computer Interface ◽

Beta Band ◽

Stroke Patients

OBJECTIVES/SPECIFIC AIMS: The objective of this study is to determine the degree to which the use of a contralesionally-controlled brain-computer interface for stroke rehabilitation drives change in interhemispheric motor cortical activity. METHODS/STUDY POPULATION: Ten chronic stroke patients were trained in the use of a brain-computer interface device for stroke recovery. Patients perform motor imagery to control the opening and closing of a motorized hand orthosis. This device was sent home with patients for 12 weeks, and patients were asked to use the device 1 hour per day, 5 days per week. The Action Research Arm Test (ARAT) was performed at 2-week intervals to assess motor function improvement. Before the active motor imagery task, patients were asked to quietly rest for 90 seconds before the task to calibrate recording equipment. EEG signals were acquired from 2 electrodes—one each centered over left and right primary motor cortex. Signals were preprocessed with a 60 Hz notch filter for environmental noise and referenced to the common average. Power envelopes for 1 Hz frequency bands (1–30 Hz) were calculated through Gabor wavelet convolution. Correlations between electrodes were then calculated for each frequency envelope on the first and last 5 runs, thus generating one correlation value per subject, per run. The chosen runs approximately correspond to the first and last week of device usage. These correlations were Fisher Z-transformed for comparison. The first and last 5 run correlations were averaged separately to estimate baseline and final correlation values. A difference was then calculated between these averages to determine correlation change for each frequency. The relationship between beta-band correlation changes (13–30 Hz) and the change in ARAT score was determined by calculating a Pearson correlation. RESULTS/ANTICIPATED RESULTS: Beta-band inter-electrode correlations tended to decrease more in patients achieving greater motor recovery (Pearson’s r=−0.68, p=0.031). A similar but less dramatic effect was observed with alpha-band (8–12 Hz) correlation changes (Pearson’s r=−0.42, p=0.22). DISCUSSION/SIGNIFICANCE OF IMPACT: The negative correlation between inter-electrode power envelope correlations in the beta frequency band and motor recovery indicates that activity in the motor cortex on each hemisphere may become more independent during recovery. The role of the unaffected hemisphere in stroke recovery is currently under debate; there is conflicting evidence regarding whether it supports or inhibits the lesioned hemisphere. These findings may support the notion of interhemispheric inhibition, as we observe less in common between activity in the 2 hemispheres in patients successfully achieving recovery. Future neuroimaging studies with greater spatial resolution than available with EEG will shed further light on changes in interhemispheric communication that occur during stroke rehabilitation.

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Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

10.31234/osf.io/en69j ◽

2019 ◽

Author(s):

Jennifer Stiso ◽

Marie-Constance Corsi ◽

Javier Omar Garcia ◽

Jean M Vettel ◽

Fabrizio De Vico Fallani ◽

...

Keyword(s):

Motor Imagery ◽

Brain Activity ◽

Brain Computer Interface ◽

Distributed Networks ◽

Motor Dysfunction ◽

Computer Interface ◽

Specific Method ◽

Temporal Expression ◽

And Behavior ◽

Brain Behavior

Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -- non-negative matrix factorization -- to simultaneously identify regularized, covarying subgraphs of functional connectivity and behavior, and to detect the time-varying expression of each subgraph. We find that learning is marked by distributed brain-behavior relations: swifter learners displayed many subgraphs whose temporal expression tracked performance. Learners also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in networks important for sustaining attention. After formalizing the model in the framework of network control theory, we test the model and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

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Could Brain–Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.699830 ◽

2021 ◽

Vol 15 ◽

Author(s):

Stuti Chakraborty ◽

Gianluca Saetta ◽

Colin Simon ◽

Bigna Lenggenhager ◽

Kathy Ruddy

Keyword(s):

Brain Activity ◽

Brain Computer Interface ◽

Treatment Options ◽

Body Representation ◽

Vestibular Stimulation ◽

Current Treatment ◽

Substantial Improvement ◽

Computer Interface ◽

Effective Form ◽

Brain Correlates

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person’s anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain–computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

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