Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation

Yang, Siyu; Li, Ruobing; Li, Hongtao; Xu, Ke; Shi, Yuqing; Wang, Qingyong; Yang, Tiansong; Sun, Xiaowei

doi:10.1155/2021/9967348

Cited by 40 publications

(35 citation statements)

References 92 publications

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“…Secondly, we analysed the effects of the MI–NFT paradigm in cognitive training. Although this training paradigm has shown promising results in stroke rehabilitation studies [ 3 , 4 , 31 , 32 , 33 , 34 , 35 ], it has not yet been explored in depth in cognitive training studies based on neurofeedback techniques. Since the MI–NFT paradigm allows for the development of more engaging [ 32 , 37 ] and complex training tasks, as presented in Section 2.1 , it is interesting to further investigate the influence of the MI–NFT paradigm on the cognitive state of users.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, some studies have proposed the use of a MI-based NFT paradigm (MI–NFT), instead of classical NFT, as a promising approach to achieve the desired modulation [ 3 , 4 , 30 ]. The MI–NFT paradigm is broadly extended among studies focusing on neurorehabilitation of post-stroke patients [ 3 , 4 , 31 , 32 , 33 , 34 , 35 ]. This paradigm has proven to be effective in promoting functional and structural brain plasticity and recovery of motor function [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

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Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Marcos-Martínez

Martínez-Cagigal

Santamaría-Vázquez

et al. 2021

Entropy

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Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI–NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject’s scores from Luria tests performed before and after MI–NFT. We found that MI–NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI–NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI–NFT.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Marcos-Martínez

Martínez-Cagigal

Santamaría-Vázquez

et al. 2021

Entropy

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“…Brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) have been extensively researched in recent decades to assist challenged individuals in communicating with the outside world [2]. Various BCI applications, such as mental spellers [3], patient-assistant systems [4], neurorehabilitation [5], and external device control [6] have been developed for these people, showing the potential of EEG-based BCIs as practical assistance tools.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

Riaz¹,

Ali²,

Anwar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Electroencephalogram (EEG) is a method of capturing the electrophysiological signal of the brain. An EEG headset is a wearable device that records electrophysiological data from the brain. This paper presents the design and fabrication of a customized low-cost Electroencephalogram (EEG) headset based on the open-source OpenBCI Ultracortex Mark IV system. The electrode placement locations are modified under a 10-20 standard system. The fabricated headset is then compared to commercially available headsets based on the following parameters: affordability, accessibility, noise, signal quality, and cost. First, the data is recorded from 20 subjects who used the EEG Headset, and signals were recorded. Secondly, the participants marked the accuracy, set up time, participant comfort, and participant perceived ease of set-up on a scale of 1 to 7 (7 being excellent). Thirdly, the self-designed EEG headband is used by 5 participants for slide changing. The raw EEG signal is decomposed into a series of band signals using discrete wavelet transform (DWT). Lastly, these findings have been compared to previously reported studies. We concluded that when used for slide-changing control, our self-designed EEG headband had an accuracy of 82.0 percent. We also concluded from the results that our headset performed well on the cost-effectiveness scale, had a reduced setup time of 2 ± 0.5 min (the shortest among all being compared), and demonstrated greater ease of use.

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“…Many clinical studies stated a remarkable enhancement in motor recovery by using BCI rehabilitation systems. Furthermore, recent review articles stated that for stroke patients’ rehabilitation methods, clinical research using the BCI rehabilitation system recorded higher clinical scores under controlled conditions [ 7 , 8 , 9 ]. Although all of these are inspiring advantages for the BCI rehabilitation systems, there are some remaining obstacles, such as the accuracy of the patient detected, motor intention, system stability cross subjects, and the different rehabilitation sessions in the accuracy of real time and real-time brain data processing techniques [ 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

et al. 2022

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Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.

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Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation

Cited by 40 publications

References 92 publications

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

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