scholarly journals Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4312
Author(s):  
Daniela Camargo-Vargas ◽  
Mauro Callejas-Cuervo ◽  
Stefano Mazzoleni
Keyword(s):  
Systematic Review ◽  
Processing System ◽  
Lower Limbs ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Motor Intention ◽  
Limb Rehabilitation ◽  
Feedback Techniques ◽  
Lower Limb Rehabilitation

In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.

Lower-Limb Rehabilitation at Home

2021 ◽  
Vol 13 (2) ◽  
pp. 12-23
Author(s):  
Seanglidet Yean ◽  
Bu-Sung Lee ◽  
Chai Kiat Yeo
Keyword(s):  
Lower Limbs ◽  
Ageing Population ◽  
Joint Angles ◽  
Functional Activities ◽  
State Identification ◽  
Motion State ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation ◽  
Rehabilitation Exercise ◽  
At Home

Ageing causes loss of muscle strength, especially on the lower limbs, resulting in higher risk to injuries during functional activities. The path to recovery is through physiotherapy and adopt customized rehabilitation exercise to assist the patients. Hence, lowering the risk of incorrect exercise at home involves the use of biofeedback for physical rehabilitation patients and quantitative reports for clinical physiotherapy. This research topic has garnered much attention in recent years owing to the fast ageing population and the limited number of clinical experts. In this paper, the authors survey the existing works in exercise assessment and state identification. The evaluation results in the accuracy of 95.83% average classifying exercise motion state using the proposed raw signal. It confirmed that raw signals have more impact than using sensor-fused Euler and joint angles in the state identification prediction model.

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

2020 ◽  
pp. 2150003
Author(s):  
Pasquale Arpaia ◽  
Francesco Donnarumma ◽  
Antonio Esposito ◽  
Marco Parvis
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Binary Classification ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Selection For ◽  
A Performance

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77–83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.

scholarly journals Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171431-171451 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Fan Zeming ◽  
Ateeq Ur Rehman ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Empirical Wavelet Transform

scholarly journals Design and Simulation Analysis of a Robot-Assisted Gait Trainer with the PBWS System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiancheng (Charles) Ji ◽  
Yufeng Wang ◽  
Guoqing Zhang ◽  
Yuanyuan Lin ◽  
Guoxiang Wang
Keyword(s):  
Lower Limb ◽  
Degrees Of Freedom ◽  
Gait Cycle ◽  
Simulation Analysis ◽  
The Elderly ◽  
Lower Limbs ◽  
Robot Assisted ◽  
Limb Rehabilitation ◽  
Gait Trainer ◽  
Lower Limb Rehabilitation

In response to the ever-increasing demand of lower limb rehabilitation, this paper presents a novel robot-assisted gait trainer (RGT) to assist the elderly and the pediatric patients with neurological impairments in the lower limb rehabilitation training (LLRT). The RGT provides three active degrees of freedom (DoF) to both legs that are used to implement the gait cycle in such a way that the natural gait is not significantly affected. The robot consists of (i) the partial body weight support (PBWS) system to assist patients in sit-to-stand transfer via the precision linear rail system and (ii) the bipedal end-effector (BE) to control the motions of lower limbs via two mechanical arms. The robot stands out for multiple modes of training and optimized functional design to improve the quality of life for those patients. To analyze the performance of the RGT, the kinematic and static models are established in this paper. After that, the reachable workspace and motion trajectory are analyzed to cover the motion requirements and implement natural gait cycle. The preliminary results demonstrate the usability of the robot.

scholarly journals Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 224
Author(s):  
Xusheng Wang ◽  
Yongfei Feng ◽  
Jiazhong Zhang ◽  
Yungui Li ◽  
Jianye Niu ◽  
...  
Keyword(s):  
Lower Limb ◽  
Interventional Therapy ◽  
The Body ◽  
Lower Limbs ◽  
Linkage Mechanism ◽  
Training Module ◽  
Stroke Patients ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Carrying out the immediate rehabilitation interventional therapy will better improve the curative effect of rehabilitation therapy, after the condition of bedridden stroke patients becomes stable. A new lower limb rehabilitation training module, as a component of a synchronous rehabilitation robot for bedridden stroke patients’ upper and lower limbs, is proposed. It can electrically adjust the body shape of patients with a different weight and height. Firstly, the innovative mechanism design of the lower limb rehabilitation training module is studied. Then, the mechanism of the lower limb rehabilitation module is simplified and the geometric relationship of the human–machine linkage mechanism is deduced. Next, the trajectory planning and dynamic modeling of the human–machine linkage mechanism are carried out. Based on the analysis of the static moment safety protection of the human–machine linkage model, the motor driving force required in the rehabilitation process is calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient’s lower limb. To reconstruct the patient’s motor functions, an active training control strategy based on the sandy soil model is proposed. Finally, the experimental platform of the proposed robot is constructed, and the preliminary physical experiment proves the feasibility of the lower limb rehabilitation component.

Classification of EEG Signals Based on Filter Bank and Sparse Representation in Motor Imagery Brain-Computer Interfaces

2019 ◽  
Vol 29 (03) ◽  
pp. 2050034 ◽  
Author(s):  
Jin Wang ◽  
Qingguo Wei
Keyword(s):  
Sparse Representation ◽  
Motor Imagery ◽  
Filter Bank ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Fisher Score ◽  
Common Spatial Pattern ◽  
Signal Processing Algorithm ◽  
Computer Interfaces

To improve the classification performance of motor imagery (MI) based brain-computer interfaces (BCIs), a new signal processing algorithm for classifying electroencephalogram (EEG) signals by combining filter bank and sparse representation is proposed. The broadband EEG signals of 8–30[Formula: see text]Hz are segmented into 10 sub-band signals using a filter bank. EEG signals in each sub-band are spatially filtered by common spatial pattern (CSP). Fisher score combined with grid search is used for selecting the optimal sub-band, the band power of which is employed for designing a dictionary matrix. A testing signal can be sparsely represented as a linear combination of some columns of the dictionary. The sparse coefficients are estimated by [Formula: see text] norm optimization, and the residuals of sparse coefficients are exploited for classification. The proposed classification algorithm was applied to two BCI datasets and compared with two traditional broadband CSP-based algorithms. The results showed that the proposed algorithm provided superior classification accuracies, which were better than those yielded by traditional algorithms, verifying the efficacy of the present algorithm.

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