Prognostic and Monitory EEG-Biomarkers for BCI Upper-Limb Stroke Rehabilitation

Mane, Ravikiran; Chew, Effie; Phua, Kok Soon; Ang, Kai Keng; Robinson, Neethu; Vinod, A. P.; Guan, Cuntai

doi:10.1109/tnsre.2019.2924742

Cited by 79 publications

(73 citation statements)

References 64 publications

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“…Learning processes in Neurofeedback have also been described with much higher orders (Gunkelman & Johnstone, 2005 Four patients of the control group showed a decline of their motor function regardless of the dynamics of their ERD throughout the intervention (two squares below the zero line in Figure 2). It has been suggested that the contingency of brain-activity and visuoproprioceptive feedback is key to cortical reorganization and recovery (Mane et al, 2019). Studies of oscillatory brain activity during motor imagery and movement of the paretic hand of moderately to severely affected chronic stroke patients (Kaiser et al, 2012) as well as subacute patients of mild to moderate (Platz et al, 2002) and severe impairment (Pichiorri et al, 2015) support our findings suggesting that the level of impairment is negatively correlated to the desynchronization of alpha oscillations on the ipsilesional hemisphere.…”

Section: Prediction Of δCfma From Interhemispheric Asymmetry Of Brasupporting

confidence: 81%

“…In heterogeneous conditions such as stroke, biomarkers could play an important role in informing treatment pathways (informed patients stratification). Mane et al have recently shown that the predictive power of EEG‐based markers may be specific to the intervention methodology (Mane et al, ). Studies of oscillatory brain activity during motor imagery and movement of the paretic hand of moderately to severely affected chronic stroke patients (Kaiser et al, ) as well as subacute patients of mild to moderate (Platz et al, ) and severe impairment (Pichiorri et al, ) support our findings suggesting that the level of impairment is negatively correlated to the desynchronization of alpha oscillations on the ipsilesional hemisphere.…”

Section: Discussionmentioning

confidence: 99%

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Brain oscillatory activity as a biomarker of motor recovery in chronic stroke

Ray

Figueiredo

López‐Larraz

et al. 2019

Human Brain Mapping

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In the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.

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Section: Prediction Of δCfma From Interhemispheric Asymmetry Of Brasupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Brain oscillatory activity as a biomarker of motor recovery in chronic stroke

Ray

Figueiredo

López‐Larraz

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…However, the movement-related brain activity change patterns differ greatly in stroke patients compared to healthy patients, and algorithms that were developed with healthy subjects may not apply to stroke patients. Stroke rehabilitation BCI technology must be based on studies involving stroke patients ( Irimia et al, 2017 ; Mane et al, 2019 ). The present study provides comprehensive information about the features of movement-related brain activity changes in stroke patients, which may contribute to the research repository.…”

Section: Discussionmentioning

confidence: 99%

EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients

Huang

Lin

et al. 2020

Front. Neurosci.

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This study investigated electroencephalogram (EEG) changes during movement preparation and execution in stroke patients. EEG-based event-related potential (ERP) technology was used to measure brain activity changes. Seventeen stroke patients participated in this study and completed ERP tests that were designed to measure EEG changes during unilateral upper limb movements in preparation and execution stages, with Instruction Response Movement (IRM) and Cued Instruction Response Movement (CIRM) paradigms. EEG data were analyzed using motor potential (MP) in the time domain and the mu-rhythm and beta frequency band response mean value (R-means) in the time–frequency domain. In IRM, the MP amplitude at Cz was higher during hemiplegic arm movement than during unaffected arm movement. MP latency was shorter at Cz and the contralesional motor cortex during hemiplegic arm movement in CIRM compared to IRM. No significant differences were found in R-means among locations, between movement sides in both ERP tests. This study presents the brain activity changes in the time and time–frequency domains in stroke patients during movement preparation and execution and supports the contralesional compensation and adjacent-region compensation mechanism of post-stroke brain reconstruction. These findings may contribute to future rehabilitation research about neuroplasticity and technology development such as the brain–computer interface.

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“…As age is the most significant risk factor in stroke, rehabilitation can help for restoring the ability for the motor functions. Prediction and monitoring of specific biomarkers of the motor function are being investigated to personalize the rehabilitation program ( Mane et al, 2019 ). Stroke is also associated with mental fatigue, and memory issues.…”

Section: How Can Bci Applications Improve the Quality Of Elderly Livimentioning

confidence: 99%

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

et al. 2020

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All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.

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Prognostic and Monitory EEG-Biomarkers for BCI Upper-Limb Stroke Rehabilitation

Cited by 79 publications

References 64 publications

Brain oscillatory activity as a biomarker of motor recovery in chronic stroke

Brain oscillatory activity as a biomarker of motor recovery in chronic stroke

EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

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