fNIRS-based brain-computer interfaces: a review

Frontiers in Human Neuroscience, Journal Year: 2015, Volume and Issue: 9

Published: Jan. 28, 2015

A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing peripheral nervous system, provide means for people suffering from severe motor disabilities in persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques fNIRS-based BCI are reviewed. The most common areas fNIRS primary cortex prefrontal cortex. relation cortex, imagery tasks were preferred execution since possible proprioceptive feedback could be avoided. showed significant advantage due no hair detecting cognitive like mental arithmetic, music imagery, emotion induction, etc. removing physiological data, band-pass filtering was mostly used. However, more advanced adaptive filtering, independent component analysis, multi optodes arrangement, being pursued overcome problem filter cannot used when both signals occur within close band. extracting features related desired signal, mean, variance, peak value, slope, skewness, kurtosis noised-removed hemodynamic response For classification, linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, will widely monitor occurrence neuro-plasticity after neuro-rehabilitation neuro-stimulation. Technical breakthroughs future expected via bundled-type probes, hybrid EEG-fNIRS BCI, through detection initial dips.

Language: Английский

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A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke

Clinical EEG and Neuroscience, Journal Year: 2014, Volume and Issue: 46(4), P. 310 - 320

Published: April 21, 2014

Electroencephalography (EEG)–based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore function by inducing activity-dependent brain plasticity. The purpose of this study was investigate efficacy an EEG-based MI BCI system coupled with MIT-Manus shoulder-elbow robotic feedback (BCI-Manus) for subjects chronic stroke upper-limb hemiparesis. In single-blind, randomized trial, 26 hemiplegic (Fugl-Meyer Assessment Motor Recovery After Stroke [FMMA] score, 4-40; 16 men; mean age, 51.4 years; duration, 297.4 days), prescreened ability use BCI, were randomly allocated BCI-Manus or Manus therapy, lasting 18 hours over 4 weeks. Efficacy measured using upper-extremity FMMA scores at weeks 0, 2, and 12. ElEG data from quantified revised symmetry index (rBSI) analyzed correlation improvements in score. Eleven 15 underwent respectively. One subject group dropped out. Mean total 4, 12 improved both groups: 26.3 ± 10.3, 27.4 12.0, 30.8 13.8, 31.5 13.5 26.6 18.9, 29.9 20.6, 32.9 21.4, 33.9 20.2 Manus, no intergroup differences ( P = .51). More attained further gains week (7 11 [63.6%]) than (5 14 [35.7%]). A negative found between rBSI score improvement .044). therapy well tolerated not associated adverse events. conclusion, is effective safe arm rehabilitation after severe poststroke comparable those intensive (1,040 repetitions/session) despite reduced exercise repetitions MI-triggered (136 repetitions/session). suggests that can be used as a prognostic measure BCI-based rehabilitation.

Language: Английский

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Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

Annals of Clinical and Translational Neurology, Journal Year: 2018, Volume and Issue: 5(5), P. 651 - 663

Published: March 25, 2018

Abstract Brain‐computer interfaces ( BCI s) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number recent clinical studies indicate that repeated use such s might trigger neurological recovery and hence improvement in motor function. Here, we a first meta‐analysis evaluating the effectiveness ‐based post‐stroke rehabilitation. Trials were identified using MEDLINE , CENTRAL PED ro by inspection references several review articles. We selected randomized controlled trials used for rehabilitation provided impairment scores before after intervention. random‐effects inverse variance method was calculate summary effect size. initially 524 articles and, removing duplicates, screened titles abstracts 473 found 26 corresponding trials, these, there nine involved total 235 fulfilled inclusion criterion (randomized examined performance as an outcome measure) meta‐analysis. Motor improvements, mostly quantified upper limb Fugl‐Meyer Assessment FMA ‐ UE ), exceeded minimal clinically important difference MCID =5.25) six studies, while reached only three control groups. Overall, training associated with standardized mean 0.79 (95% CI : 0.37 1.20) compared conditions, which is range medium large In addition, indicated ‐induced functional structural neuroplasticity at subclinical level. This suggests technology could be effective intervention However, more larger sample size are required increase reliability these results.

Language: Английский

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BCI for stroke rehabilitation: motor and beyond

Journal of Neural Engineering, Journal Year: 2020, Volume and Issue: 17(4), P. 041001 - 041001

Published: July 2, 2020

Abstract Stroke is one of the leading causes long-term disability among adults and contributes to major socio-economic burden globally. frequently results in multifaceted impairments including motor, cognitive emotion deficits. In recent years, brain–computer interface (BCI)-based therapy has shown promising for post-stroke motor rehabilitation. spite success received by BCI-based interventions domain, non-motor are yet receive similar attention research clinical settings. Some preliminary encouraging rehabilitation using BCI seem suggest that it may also hold potential treating deficits such as impairments. Moreover, past studies have an intricate relationship between functions which might influence overall outcome. A number highlight inability current treatment protocols account implicit interplay functions. This indicates necessity explore all-inclusive plan targeting synergistic these standalone interventions. approach lead better recovery than individual isolation. this paper, we review advances use systems beyond particular, improving cognition stroke patients. Building on findings domains, next discuss possibility a holistic system affect synergistically promote restorative neuroplasticity. Such would provide all-encompassing platform, overarching outcomes transfer quality living. first works analyse cross-domain functional enabled

Language: Английский

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Brain–machine interfaces in neurorehabilitation of stroke

Neurobiology of Disease, Journal Year: 2014, Volume and Issue: 83, P. 172 - 179

Published: Dec. 7, 2014

Stroke is among the leading causes of long-term disabilities leaving an increasing number people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve first weeks months, further recovery often slow or non-existent more severe cases encompassing 30–50% all victims. The neurobiological mechanisms underlying those patients are incompletely understood. However, recent studies demonstrated brain's remarkable capacity for functional structural plasticity even chronic stroke. As established rehabilitation strategies require some remaining function, there currently no standardized accepted treatment complete muscle paralysis. development brain–machine interfaces (BMIs) that translate brain activity into control signals computers external devices provides two new to overcome stroke-related First, BMIs establish continuous high-dimensional brain-control robotic electric stimulation (FES) assist life activities (assistive BMI). Second, could facilitate neuroplasticity, thus enhancing learning (rehabilitative Advances sensor technology, non-invasive implantable wireless BMI-systems combination stimulation, along evidence BMI systems' clinical efficacy suggest BMI-related will play role neurorehabilitation

Language: Английский

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Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke

Frontiers in Neuroengineering, Journal Year: 2014, Volume and Issue: 7

Published: July 29, 2014

The objective of this study was to investigate the efficacy an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic patients (Fugl-Meyer Assessment (FMMA) score 10-50), recruited after pre-screening MI BCI ability, were randomly allocated BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions intervention over 6 weeks, 3 per week, 90 min session. BCI-HK group 1 h intervention, and Both 120 trials robot-assisted hand grasping knob manipulation followed by 30 therapist-assisted mobilization. SAT 1.5 mobilization forearm pronation-supination movements incorporating wrist control grasp-release functions. all, 14 males, 7 females, mean age 54.2 years, duration 385.1 days, baseline FMMA 27.0 recruited. primary outcome measure upper extremity scores measured mid-intervention at week 3, end-intervention 6, follow-up weeks 12 24. Seven, 8 subjects underwent interventions respectively. improved all groups, but no intergroup differences found any time points. Significantly larger motor gains observed compared 12, 24, did not differ from point. conclusion, is effective, safe, may have potential enhancing recovery when combined

Language: Английский

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The self-regulating brain and neurofeedback: Experimental science and clinical promise

Cortex, Journal Year: 2015, Volume and Issue: 74, P. 247 - 261

Published: Nov. 18, 2015

Language: Английский

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Brain-Computer Interface in Stroke Rehabilitation

Journal of Computing Science and Engineering, Journal Year: 2013, Volume and Issue: 7(2), P. 139 - 146

Published: June 30, 2013

Recent advances in computer science enabled people with severe motor disabilities to use brain-computer interfaces (BCI) for communication, control, and even restore their disabilities. This paper reviews the most recent works of BCI stroke rehabilitation a focus on methodology that reported data collected from patients clinical studies improvements patients. Both types are important as former technology stroke, latter demonstrates efficacy stroke. Finally some challenges discussed.

Language: Английский

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Connectivity measures are robust biomarkers of cortical function and plasticity after stroke

Brain, Journal Year: 2015, Volume and Issue: 138(8), P. 2359 - 2369

Published: June 11, 2015

Valid biomarkers of motor system function after stroke could improve clinical decision-making. Electroencephalography-based measures are safe, inexpensive, and accessible in complex medical settings so attractive candidates. This study examined specific electroencephalography cortical connectivity as by assessing their relationship with deficits across 28 days intensive therapy. Resting-state were acquired four times using dense array (256 leads) 12 hemiparetic patients (7.3 ± 4.0 months post-stroke, age 26–75 years, six male/six female) therapy targeting arm deficits. Structural magnetic resonance imaging measured corticospinal tract injury infarct volume. At baseline, leads overlying ipsilesional primary cortex (M1) was a robust marker status, accounting for 78% variance impairment; M1 frontal-premotor (PM) regions accounted most this (R2 = 0.51) remained significant controlling injury. Baseline impairment also correlated 0.52), though not A model that combined functional measure structural (corticospinal injury) performed better than either alone 0.93). Across the therapy, change good biomarker gains 0.61). Ipsilesional M1–PM increased parallel gains, greater associated larger increases 0.34); decreases M1–parietal 0.36). In sum, connectivity—particularly between premotor—are strongly related to improvement may be useful plasticity. Such might provide biological approach distinguishing patient subgroups stroke. would assist tailoring optimisation treatment. Wu et al. show EEG – particularly correlate over course

Language: Английский

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The sensory side of post-stroke motor rehabilitation

Restorative Neurology and Neuroscience, Journal Year: 2016, Volume and Issue: 34(4), P. 571 - 586

Published: Aug. 13, 2016

Contemporary strategies to promote motor recovery following stroke focus on repetitive voluntary movements. Although successful movement relies efficient sensorimotor integration, functional outcomes often bias therapy toward motor-related impairments such as weakness, spasticity and syner gies; sensory reintegration is implied, but seldom targeted. However, the planning execution of requires that brain extracts information regarding body position predicts future positions, by integrating a variety inputs with ongoing planned activity. Neurological patients who have lost one or more their senses may show profoundly affected functions, even if muscle strength remains unaffected. Following stroke, can be dictated degree disruption. Consequently, thorough account function might both prognostic prescriptive in neurorehabilitation. This review outlines key components human movement, describes how disruption influence prognosis expected patients, reports current sensory-based approaches post-stroke rehabilitation, makes recommendations for optimizing rehabilitation programs based stimulation.

Language: Английский

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