A review of feature selection methods in medical applications

Computers in Biology and Medicine, Journal Year: 2019, Volume and Issue: 112, P. 103375 - 103375

Published: July 31, 2019

Language: Английский

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Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Physiological Reviews, Journal Year: 2017, Volume and Issue: 97(2), P. 767 - 837

Published: March 9, 2017

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, engineering in an effort to establish real-time bidirectional links between living brains artificial actuators. Although theoretical propositions some proof of concept experiments on directly linking the with machines date back early 1960s, BMI research only took off earnest at end 1990s, when this approach became intimately linked new neurophysiological methods for sampling large-scale brain activity. The classic goals BMIs are 1) unveil utilize principles operation plastic properties distributed dynamic circuits 2) create therapies restore mobility sensations severely disabled patients. Over past decade, a wide range applications have emerged, which considerably expanded these original goals. studies shown neural control over movements robotic virtual actuators that enact both upper lower limb functions. Furthermore, also incorporated ways deliver sensory feedback, generated external actuators, brain. has been forefront many discoveries, including demonstration that, through continuous use, tools can be assimilated by primate brain's body schema. Work led introduction novel neurorehabilitation strategies. As result efforts, long-term use recently implicated induction partial neurological recovery spinal cord injury

Language: Английский

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EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Sensors, Journal Year: 2019, Volume and Issue: 19(6), P. 1423 - 1423

Published: March 22, 2019

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines movement of limb. This paper reviews state-of-the-art signal processing techniques for EEG-based BCIs, with particular focus on feature extraction, selection classification used. It also summarizes main applications based finally presents detailed discussion most prevalent challenges impeding development commercialization BCIs.

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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Novel Stroke Therapeutics: Unraveling Stroke Pathophysiology and Its Impact on Clinical Treatments

Neuron, Journal Year: 2015, Volume and Issue: 87(2), P. 297 - 309

Published: July 1, 2015

Language: Английский

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Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial

Frontiers in Neuroscience, Journal Year: 2017, Volume and Issue: 11

Published: July 20, 2017

Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback brain activity were recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only few clinical studies that investigate feasibility and effectiveness such an approach. Here we report on placebo-controlled, multicenter trial investigated whether survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up 40 minutes. A total 74 patients participated: median time since is 8 months, 25% 75% quartiles [3.0; 13.0]; severity UL 4.5 points [0.0; 30.0] measured by the Action Research Arm Test , ARAT, 19.5 [11.0; 40.0] Fugl-Meyer Motor Assessment, FMMA. Patients in group (n=55) performed imagery opening their affected hand. imagery-related electric was translated into hand exoskeleton-driven movements In control (n=19), independent activity. Evaluation assessments indicated both groups improved, but showed improvement ARAT's grasp score 0 14.0] 3.0 15.0] (p<0.001) pinch scores 0.0 7.0] 1.0 12.0] (p<0.001). Upon completion, 21.8% (36.4%) improved ARAT (FMMA) scores. The corresponding numbers for 5.3% (ARAT) 15.8% (FMMA). These results suggests adding exoskeleton-assisted physical therapy can improve post-stroke outcomes. Both maximum mean values percentage successfully decoded EEG activity, higher than chance level. correlation between classification accuracy extremity found. An found all independently duration, location stroke. Clinical registration number: NCT02325947.

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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A decade retrospective of medical robotics research from 2010 to 2020

Science Robotics, Journal Year: 2021, Volume and Issue: 6(60)

Published: Nov. 10, 2021

Eighty percent of medical robotics papers have been published in the past decade—What has accomplished?

Language: Английский

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