Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices

Lazcano-Herrera, Alicia Guadalupe; Fuentes-Aguilar, Rita Q.; Chairez, Isaac; Alonso-Valerdi, Luz María; González-Mendoza, Miguel; Alfaro-Ponce, Mariel

doi:10.3390/app122312253

Cited by 10 publications

(1 citation statement)

References 95 publications

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“…MRCPs have been successfully studied for detection using many trials and a single trial [ 20 ], making them potentially promising neural control signals. BCI with MRCP as a control signal in neural rehabilitation applications to induce plasticity by monitoring movement intent and feedback is researched [ 21 ]. The correlation of MRCP signals in detecting different types of movements and distinguishing between rest and motion has been demonstrated in [ 16 , 22 ], which is why MRCP signals are used in rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials

et al. 2022

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To enhance the treatment of motor function impairment, patients’ brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain–computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.

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Section: Introductionmentioning

confidence: 99%

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials

et al. 2022

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Spatiotemporal feature extraction method combining common spatial pattern and hybrid spatial‐temporal attention‐Net in motor imagery–brain‐computer interface system

Qin,

Li,

Yang

et al. 2024

Journal of Intelligent Medicine

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In recent years, motor imagery (MI) based on brain‐computer interface (BCI) has gained the attention of researchers and been widely used in many fields, such as medical rehabilitation and entertainment. The temporal and spatial features of electroencephalography (EEG) are very important for MI‐BCI classification. This study proposed a better spatiotemporal feature extraction method by combining the common spatial pattern (CSP) with a hybrid spatiotemporal attention convolutional neural network (HSTA‐Net) model. The method first intercepts the MI‐EEG into multiple time windows and projects them into the feature space and feeds into the HSTA‐Net in parallel. Subsequently, the scaled dot product attention module and multi‐head attention module are used to extract spatial feature attention. Finally, the features in multiple time windows obtained through HSTA‐Net are integrated and used for classification. The proposed method achieves a classification accuracy of 77.3% on the BCI Competition IV2a dataset. The experimental results show that the proposed model has better classification performance. The CSP‐HSTA‐Net model can quickly adapt to the distribution of MI‐EEG data during the training process, which increases the interclass distance of the extracted features and decreases its intraclass distance; it can better accomplish the MI‐BCI classification task.

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Revolutionizing Stroke Rehabilitation: Integrating Technology and Automation for Enhanced Patient Outcomes

Abdulaziz,

Loqman

2024

Lecture Notes in Networks and Systems

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Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices

Cited by 10 publications

References 95 publications

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain–Computer Interface Based on Movement-Related Cortical Potentials

Spatiotemporal feature extraction method combining common spatial pattern and hybrid spatial‐temporal attention‐Net in motor imagery–brain‐computer interface system

Revolutionizing Stroke Rehabilitation: Integrating Technology and Automation for Enhanced Patient Outcomes

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