Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke

Proceedings of the IEEE, Год журнала: 2015, Номер 103(6), С. 944 - 953

Опубликована: Май 12, 2015

Current rehabilitation therapies for stroke rely on physical practice (PP) by the patients. Motor imagery (MI), imagination of movements without action, presents an alternate neurorehabilitation patients relying residue movements. However, MI is endogenous mental process that not physically observable. Recently, advances in brain-computer interface (BCI) technology have enabled objective detection spearheaded this stroke. In review, we present two strategies using BCI after stroke: detecting to trigger a feedback, and with robot provide concomitant PP. We also three randomized control trials employed these upper limb rehabilitation. A total 125 chronic were screened over six years. The screening revealed 103 (82%) can use electroencephalogram-based BCI, 75 (60%) performed well accuracies above 70%. 67 recruited complete one RCTs ranging from weeks which 26 patients, who underwent strategies, had significant motor improvement 4.5 measured Fugl-Meyer Assessment extremity. Hence, results demonstrate clinical efficacy as

Язык: Английский

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A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke

Expert Review of Medical Devices, Год журнала: 2016, Номер 13(5), С. 445 - 454

Опубликована: Апрель 26, 2016

Stroke is a leading cause of acquired disability resulting in distal upper extremity functional motor impairment. mortality rates continue to decline with advances healthcare and medical technology. This has led an increased demand for advanced, personalized rehabilitation. Survivors often experience some level spontaneous recovery shortly after their stroke event, yet reach plateau which there exiguous recovery. Nevertheless, studies have demonstrated the potential beyond this plateau. Non-traditional neurorehabilitation techniques, such as those incorporating brain-computer interface (BCI), are being investigated BCIs may offer gateway brain's plasticity revolutionize how humans interact world. Non-invasive work by closing proprioceptive feedback loop real-time, multi-sensory allowing volitional modulation brain signals assist hand function. BCI technology potentially promotes neuroplasticity Hebbian-based rewarding cortical activity associated sensory-motor rhythms through use variety self-guided assistive modalities.

Язык: Английский

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EEG Classification of Different Imaginary Movements within the Same Limb

PLoS ONE, Год журнала: 2015, Номер 10(4), С. e0121896 - e0121896

Опубликована: Апрель 1, 2015

The task of discriminating the motor imagery different movements within same limb using electroencephalography (EEG) signals is challenging because these imaginary have close spatial representations on cortex area. There is, however, a pressing need to succeed in this task. reason that ability classify same-limb could increase number control dimensions brain-computer interface (BCI). In paper, we propose 3-class BCI system discriminates EEG corresponding rest, grasp movements, and elbow movements. Besides, differences between simple goal-oriented terms their topographical distributions classification accuracies are also being investigated. To best our knowledge, both problems not been explored literature. Based data recorded from 12 able-bodied individuals, demonstrated possible. For binary (goal-oriented) average accuracy achieved 66.9%. problem rest against 60.7%, which greater than random 33.3%. Our results show lead better performance compared This proposed potentially be used controlling robotic rehabilitation system, can assist stroke patients performing task-specific exercises.

Язык: Английский

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An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

Frontiers in Neurorobotics, Год журнала: 2019, Номер 13

Опубликована: Март 29, 2019

Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity restore function and improve quality of life stroke survivors. However, it is often difficult BCI systems provide the variety control commands necessary multi-task real-time soft robot naturally. In this study, a novel multimodal human-machine system (mHMI) developed using combinations electrooculography (EOG), electroencephalography (EEG), electromyogram (EMG) generate numerous instructions. Moreover, we also explore subject acceptance an affordable wearable move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left right imagery, looking-left looking-right eye movements, different gestures in modes actions. The results indicate number mHMI instructions significantly greater than achievable with any individual mode. Furthermore, can achieve average classification accuracy 93.83% information transfer rate 47.41 bits/min, which entirely equivalent speed 17 per minute. study expected construct more user-friendly help or disabled persons movements friendly convenient way.

Язык: Английский

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A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity

PLoS ONE, Год журнала: 2019, Номер 14(1), С. e0207351 - e0207351

Опубликована: Янв. 25, 2019

Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from large-scale screening study conducted on 80 novice participants with Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one session resting state Encephalography, Motor Observation, Execution Imagery recordings 128 electrodes. A significant portion (40%) could not achieve control (feedback performance > 70%). Based calibration feedback runs, users stratified three groups. Analyses directed to detect elucidate differences SMR activity these groups performed. Statistics reactive frequencies, task prevalence classification results reported. their activity, also systematic list potential reasons leading drops thus hints possible improvements experimental design given. The categorization has several advantages, allowing researchers 1) select subjects further analyses as well testing new paradigms or algorithms, 2) adopt better subject-dependent training strategy 3) easier comparisons between different studies.

Язык: Английский

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A review of user training methods in brain computer interfaces based on mental tasks

Journal of Neural Engineering, Год журнала: 2020, Номер 18(1), С. 011002 - 011002

Опубликована: Ноя. 12, 2020

Abstract Mental-tasks based brain–computer interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they still barely used outside laboratories due lack of reliability. require develop the ability self-regulate specific signals. However, human learning process control a BCI is relatively poorly understood and how optimally train this currently under investigation. Despite promises achievements, traditional training programs have been shown be sub-optimal could further improved. In order optimize user improve performance, factors should taken into account. An interdisciplinary approach adopted provide learners appropriate and/or adaptive training. article, we overview existing methods MT-BCI training—notably in terms environment, instructions, feedback exercises. We present categorization taxonomy these approaches, guidelines on choose best identify open challenges perspectives

Язык: Английский

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BCI-Based Rehabilitation on the Stroke in Sequela Stage

Neural Plasticity, Год журнала: 2020, Номер 2020, С. 1 - 10

Опубликована: Дек. 13, 2020

Background. Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed best time period for recovery entered into sequela stage chronic stroke. Method. Studies have shown that imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs functional electrical stimulation (FES) as feedback to provide with closed-loop sensorimotor integration An MI-based BCI system acquired, analyzed, classified attempts from electroencephalogram (EEG) signals. The FES would be activated if detected user was imagining wrist dorsiflexion instructed side body. Sixteen in were randomly assigned group control group. All them participated training four weeks assessed by Fugl-Meyer Assessment (FMA) function. Results. average improvement score 3.5, which higher than (0.9). active EEG patterns whose FMA scores increased gradually became centralized shifted areas premotor throughout study. Conclusions. Study results showed evidence achieved larger improvements those BCI-FES effective restoring function upper extremities patients. provides more autonomous approach traditional treatments

Язык: Английский

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Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters

PLoS ONE, Год журнала: 2014, Номер 9(2), С. e87056 - e87056

Опубликована: Фев. 14, 2014

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian–and thereby probabilistic–framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, BSSFO framework allows analyze which spatio-spectral parameters are common ones differ across subject population. As expected, variability brain rhythms is observed between subjects. We have clustered subjects according similarities in their corresponding spectral characteristics from model, found reflect performances well. BCI, considerable percentage unable use communication, due missing ability modulate rhythms–a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding actual, time-consuming BCI-experiment enhances usage BCIs, e.g., by detecting users with This work additionally contributes using novel method predict BCI-performance only 2 minutes 3 channels resting-state EEG recorded before actual BCI-experiment. Specifically, grouping nicely classified them into 'prototypes' (like μ - β -rhythm type subjects) without communicate then further building linear regression model based on could maximum correlation coefficient 0.581 later seen session.

Язык: Английский

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Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG

Journal of Neural Engineering, Год журнала: 2015, Номер 12(5), С. 056013 - 056013

Опубликована: Авг. 25, 2015

Objective. To detect movement intention from executed and imaginary palmar grasps in healthy subjects attempted executions stroke patients using one EEG channel. Moreover, force speed were also decoded. Approach. Fifteen performed motor execution imagination of four types grasps. In addition, five to perform the same movements. The movements detected continuous a single electrode/channel overlying cortical representation hand. Four features extracted signal classified with support vector machine (SVM) decode level associated movement. system performance was evaluated based on both detection classification. Main results. ∼75% all (executed, attempted) 100 ms before onset ∼60% correctly according intended speed. When classification combined, ∼45% subjects, although slightly better subjects. Significance. results indicate that it is possible use channel for detecting intentions may be combined assistive technologies. simple setup lead smoother transition laboratory tests clinic.

Язык: Английский

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Electroencephalography (EEG)‐Based Brain–Computer Interfaces

Wiley Encyclopedia of Electrical and Electronics Engineering, Год журнала: 2015, Номер unknown, С. 1 - 20

Опубликована: Сен. 15, 2015

Brain–computer interfaces (BCIs) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The is processed by BCI usually measured using electroencephalography (EEG). In this article, we aim at providing accessible and up‐to‐date overview EEG‐based BCI, with main focus on its engineering aspects. We notably introduce some basic neuroscience background, explain how to design in particular reviewing which signal processing, machine learning, software hardware tools use. present applications, highlight limitations current systems, suggest perspectives field.

Язык: Английский

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