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Vidaurre C, Irastorza-Landa N, Sarasola-Sanz A, Insausti-Delgado A, Ray AM, Bibián C, Helmhold F, Mahmoud WJ, Ortego-Isasa I, López-Larraz E, Lozano Peiteado H, Ramos-Murguialday A. Challenges of neural interfaces for stroke motor rehabilitation. Front Hum Neurosci 2023; 17:1070404. [PMID: 37789905 PMCID: PMC10543821 DOI: 10.3389/fnhum.2023.1070404] [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: 10/14/2022] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.
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Affiliation(s)
- Carmen Vidaurre
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Ikerbasque Science Foundation, Bilbao, Spain
| | | | | | | | - Andreas M. Ray
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Carlos Bibián
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Florian Helmhold
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Wala J. Mahmoud
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Iñaki Ortego-Isasa
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
| | - Eduardo López-Larraz
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Bitbrain, Zaragoza, Spain
| | | | - Ander Ramos-Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
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2
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Zuo C, Jin J, Yin E, Saab R, Miao Y, Wang X, Hu D, Cichocki A. Novel hybrid brain-computer interface system based on motor imagery and P300. Cogn Neurodyn 2019; 14:253-265. [PMID: 32226566 DOI: 10.1007/s11571-019-09560-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 09/19/2019] [Accepted: 10/08/2019] [Indexed: 01/08/2023] Open
Abstract
Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.
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Affiliation(s)
- Cili Zuo
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Jin
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Erwei Yin
- Unmanned Systems Research Center, National Institute of Defense Technology Innovation, Academy of Military Sciences China, Beijing, 100081 People's Republic of China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, People's Republic of China
| | - Rami Saab
- 4Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Yangyang Miao
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xingyu Wang
- 1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Dewen Hu
- 5College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, 410073 Hunan People's Republic of China
| | - Andrzej Cichocki
- 6Skolkovo Institute of Science and Technology (SKOLTECH), Moscow, Russia 143026.,7Systems Research Institute PAS, Warsaw, Poland.,8Nicolaus Copernicus University (UMK), Torun, Poland
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Zhao Y, Han J, Chen Y, Sun H, Chen J, Ke A, Han Y, Zhang P, Zhang Y, Zhou J, Wang C. Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification. Front Neurosci 2018; 12:272. [PMID: 29867307 PMCID: PMC5954047 DOI: 10.3389/fnins.2018.00272] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 04/09/2018] [Indexed: 11/27/2022] Open
Abstract
Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.
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Affiliation(s)
- Yuwei Zhao
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
| | - Jiuqi Han
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
| | - Yushu Chen
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
| | - Hongji Sun
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
| | - Jiayun Chen
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China.,College of Life Science and Technology, Huazhong Agricultural University Wuhan, China
| | - Ang Ke
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China.,Neural Interface & Rehabilitation Technology Research Center, Huazhong University of Science and Technology Wuhan, China
| | - Yao Han
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China.,Stem Cell and Tissue Engineering Lab, Beijing Institute of Transfusion Medicine Beijing, China
| | - Peng Zhang
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China.,Neural Interface & Rehabilitation Technology Research Center, Huazhong University of Science and Technology Wuhan, China
| | - Yi Zhang
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
| | - Jin Zhou
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
| | - Changyong Wang
- Department of Neural Engineering and Biological Interdisciplinary Studies, Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences Beijing, China
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Huang G, Yang Z, Chen X, Ji G. An innovative one-class least squares support vector machine model based on continuous cognition. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.02.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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5
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Mao X, Li M, Li W, Niu L, Xian B, Zeng M, Chen G. Progress in EEG-Based Brain Robot Interaction Systems. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:1742862. [PMID: 28484488 PMCID: PMC5397651 DOI: 10.1155/2017/1742862] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 03/21/2017] [Indexed: 11/17/2022]
Abstract
The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.
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Affiliation(s)
- Xiaoqian Mao
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Mengfan Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Wei Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
- Department of Computer & Electrical Engineering and Computer Science, California State University, Bakersfield, CA 93311, USA
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, Liaoning 110016, China
| | - Linwei Niu
- Department of Math and Computer Science, West Virginia State University, Institute, WV 25112, USA
| | - Bin Xian
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Ming Zeng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Genshe Chen
- Intelligent Fusion Technology, Inc., Germantown, MD 20876, USA
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7
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Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG. APPLIED SCIENCES-BASEL 2017. [DOI: 10.3390/app7010092] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, Rojas I. Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.06.014] [Citation(s) in RCA: 161] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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9
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Duan F, Lin D, Li W, Zhang Z. Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo Module. ACTA ACUST UNITED AC 2015. [DOI: 10.1109/tamd.2015.2434951] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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10
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Ren Y, Wu Y, Ge Y. A co-training algorithm for EEG classification with biomimetic pattern recognition and sparse representation. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.05.045] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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