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Ma P, Dong C, Lin R, Liu H, Lei D, Chen X, Liu H. A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks. Front Neurosci 2024; 18:1306283. [PMID: 38586195 PMCID: PMC10996401 DOI: 10.3389/fnins.2024.1306283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Background The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. Objective This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. Methods The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. Results For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. Conclusion The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.
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Affiliation(s)
- Pengfei Ma
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
| | - Chaoyi Dong
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Ruijing Lin
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Huanzi Liu
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Dongyang Lei
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
| | - Xiaoyan Chen
- College of Electric Power, Inner Mongolia University of Technology, Hohhot, China
- Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges and Universities in Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, China
- Engineering Research Center of Large Energy Storage Technology, Ministry of Education, Hohhot, Inner Mongolia, China
| | - Huan Liu
- College of Computer and Software Engineering, Dalian Neusoft University of Information, Dalian, China
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De Miguel-Rubio A, Gallego-Aguayo I, De Miguel-Rubio MD, Arias-Avila M, Lucena-Anton D, Alba-Rueda A. Effectiveness of the Combined Use of a Brain-Machine Interface System and Virtual Reality as a Therapeutic Approach in Patients with Spinal Cord Injury: A Systematic Review. Healthcare (Basel) 2023; 11:3189. [PMID: 38132079 PMCID: PMC10742447 DOI: 10.3390/healthcare11243189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/30/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Spinal cord injury has a major impact on both the individual and society. This damage can cause permanent loss of sensorimotor functions, leading to structural and functional changes in somatotopic regions of the spinal cord. The combined use of a brain-machine interface and virtual reality offers a therapeutic alternative to be considered in the treatment of this pathology. This systematic review aimed to evaluate the effectiveness of the combined use of virtual reality and the brain-machine interface in the treatment of spinal cord injuries. A search was performed in PubMed, Web of Science, PEDro, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, and Medline, including articles published from the beginning of each database until January 2023. Articles were selected based on strict inclusion and exclusion criteria. The Cochrane Collaboration's tool was used to assess the risk of bias and the PEDro scale and SCIRE systems were used to evaluate the methodological quality of the studies. Eleven articles were selected from a total of eighty-two. Statistically significant changes were found in the upper limb, involving improvements in shoulder and upper arm mobility, and weaker muscles were strengthened. In conclusion, most of the articles analyzed used the electroencephalogram as a measurement instrument for the assessment of various parameters, and most studies have shown improvements. Nonetheless, further research is needed with a larger sample size and long-term follow-up to establish conclusive results regarding the effect size of these interventions.
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Affiliation(s)
- Amaranta De Miguel-Rubio
- Department of Nursing, Pharmacology and Physiotherapy, University of Cordoba, 14004 Cordoba, Spain; (I.G.-A.); (A.A.-R.)
| | - Ignacio Gallego-Aguayo
- Department of Nursing, Pharmacology and Physiotherapy, University of Cordoba, 14004 Cordoba, Spain; (I.G.-A.); (A.A.-R.)
| | | | - Mariana Arias-Avila
- Physical Therapy Department, Universidade Federal de São Carlos, São Paulo 13565-905, Brazil;
| | - David Lucena-Anton
- Department of Nursing and Physiotherapy, University of Cadiz, 11009 Cadiz, Spain;
| | - Alvaro Alba-Rueda
- Department of Nursing, Pharmacology and Physiotherapy, University of Cordoba, 14004 Cordoba, Spain; (I.G.-A.); (A.A.-R.)
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Hu X, Xu W, Ren Y, Wang Z, He X, Huang R, Ma B, Zhao J, Zhu R, Cheng L. Spinal cord injury: molecular mechanisms and therapeutic interventions. Signal Transduct Target Ther 2023; 8:245. [PMID: 37357239 DOI: 10.1038/s41392-023-01477-6] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/22/2023] [Accepted: 05/07/2023] [Indexed: 06/27/2023] Open
Abstract
Spinal cord injury (SCI) remains a severe condition with an extremely high disability rate. The challenges of SCI repair include its complex pathological mechanisms and the difficulties of neural regeneration in the central nervous system. In the past few decades, researchers have attempted to completely elucidate the pathological mechanism of SCI and identify effective strategies to promote axon regeneration and neural circuit remodeling, but the results have not been ideal. Recently, new pathological mechanisms of SCI, especially the interactions between immune and neural cell responses, have been revealed by single-cell sequencing and spatial transcriptome analysis. With the development of bioactive materials and stem cells, more attention has been focused on forming intermediate neural networks to promote neural regeneration and neural circuit reconstruction than on promoting axonal regeneration in the corticospinal tract. Furthermore, technologies to control physical parameters such as electricity, magnetism and ultrasound have been constantly innovated and applied in neural cell fate regulation. Among these advanced novel strategies and technologies, stem cell therapy, biomaterial transplantation, and electromagnetic stimulation have entered into the stage of clinical trials, and some of them have already been applied in clinical treatment. In this review, we outline the overall epidemiology and pathophysiology of SCI, expound on the latest research progress related to neural regeneration and circuit reconstruction in detail, and propose future directions for SCI repair and clinical applications.
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Affiliation(s)
- Xiao Hu
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Wei Xu
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Yilong Ren
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Zhaojie Wang
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Xiaolie He
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Runzhi Huang
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Bei Ma
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Jingwei Zhao
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China
| | - Rongrong Zhu
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China.
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China.
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China.
| | - Liming Cheng
- Division of Spine, Department of Orthopaedics, Tongji Hospital, Tongji University School of Medicine, 200065, Shanghai, China.
- Key Laboratory of Spine and Spinal cord Injury Repair and Regeneration (Tongji University), Ministry of Education, 200065, Shanghai, China.
- Clinical Center For Brain And Spinal Cord Research, Tongji University, 200065, Shanghai, China.
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