Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain–Computer Interface Technology

Wang, Huihai; Su, Qinglun; Yan, Zhenzhuang; Lu, Fei; Zhao, Qin; Liu, Zhen; Zhou, Fang

doi:10.3389/fnins.2020.595084

Cited by 14 publications

(7 citation statements)

References 46 publications

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“…Data from 10 volunteers showed a success rate of 70% from the raw EEG and a success rate of 96% from the spectrum. DL methods are better than machine learning algorithms when the data is complex, unstructured, abundant, and feature rich [ 89 , 90 ]. Also, DL can easily describe complex relationships and preserve the information extracted from brain networks [ 91 ] or even as [ 92 ] expressed, DL techniques are useful to infer information about the correctness of action in BCI applications.…”

Section: Discussionmentioning

confidence: 99%

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

Camargo-Vargas

Callejas-Cuervo

Mazzoleni

2021

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

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

Camargo-Vargas

Callejas-Cuervo

Mazzoleni

2021

Sensors

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“…The superiority of DL in signal processing has prompted researchers to adopt end-to-end algorithms based on the backpropagation mechanism for classification (Wang et al, 2020 ; Tang et al, 2023b ; Zhang H. et al, 2023 ; Zhang J. et al, 2023 ). A multitude of models have been devised which transfigure unprocessed EEG signals into spatial-spectral-temporal forms for categorization, including CNN (Hossain et al, 2023 ), ANN (Subasi, 2005 ), and EEGNET (Lawhern et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces

Xue,

Song,

et al. 2024

Front. Neurosci.

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IntroductionWithin the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.MethodsInspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.Results and discussionAchievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.

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“…Clear examples are subjects affected by neuromuscular disorders, such as MD, ALS, MS, SCI, and CP, or even poststroke patients and amputees [5,[18][19][20]. In this scenario, two main targets can be identified: robotic control [14,15,34,49,50,[64][65][66][67][68][69] and prosthetic control [46][47][48][69][70][71][72][73][74][75][76][77][78].…”

Section: Eeg-based Hmismentioning

confidence: 99%

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

Esposito

Centracchio

Andreozzi

et al. 2021

Sensors

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As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

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Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain–Computer Interface Technology

Cited by 14 publications

References 46 publications

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

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

Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

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