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Scalise M, Bora TS, Zancanella C, Safa A, Stefini R, Cannizzaro D. Virtual Reality as a Therapeutic Tool in Spinal Cord Injury Rehabilitation: A Comprehensive Evaluation and Systematic Review. J Clin Med 2024; 13:5429. [PMID: 39336916 PMCID: PMC11432221 DOI: 10.3390/jcm13185429] [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: 08/20/2024] [Revised: 09/03/2024] [Accepted: 09/11/2024] [Indexed: 09/30/2024] Open
Abstract
Introduction: The spinal rehabilitation process plays a crucial role in SCI patients' lives, and recent developments in VR have the potential to efficiently engage SCI patients in therapeutic activities and promote neuroplasticity. Objective: The primary objective of this study is to assess a complete review of the extended impacts of VR-assisted training on spine rehabilitation in SCI patients. Methods: This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) through a single database search in PubMed/Medline between the dates 1 January 2010 and 1 February 2024. MESH terms and keywords were combined in the following search strategy: (Augmented Reality OR VR OR Virtual Reality) AND (Spine OR Spinal) AND Rehabilitation. Included articles were written in English, involved adults with SCI, included an intervention with VR, AR, or any mixed reality system, and assessed changes in outcomes after the intervention. Results: The search produced 257 articles, and 46 of them were allocated for data extraction to evaluate 652 patients. Both when VR training was analyzed and reviewed separately, and when compared to traditional training, the findings exhibited predominantly promising outcomes, reflecting a favorable trend in the study. VR technologies were used in different settings and customizations, and the medium total time of VR training among the studies was 60.46 h per patient. Conclusions: This auspicious outcome of the study further motivates the intervention of VR and AR in the rehabilitation of SCI patients along with ameliorating their overall holistic well-being.
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Affiliation(s)
- Matteo Scalise
- Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Via Olgettina 58, 20132 Milan, Italy
| | - Tevfik Serhan Bora
- Department of Molecular Medicine, University of Pavia, Via Forlanini 14, 27100 Pavia, Italy
| | - Chiara Zancanella
- Department of Molecular Medicine, University of Pavia, Via Forlanini 14, 27100 Pavia, Italy
| | - Adrian Safa
- Department of Neurosurgery, Mayo Clinic Florida, Scottsdale, AZ 85259, USA
| | - Roberto Stefini
- Department of Neurosurgery, ASST Ovest Milano Legnano Hospital, Via Papa Giovanni Paolo II, 20025 Legnano, Italy
| | - Delia Cannizzaro
- Department of Neurosurgery, ASST Ovest Milano Legnano Hospital, Via Papa Giovanni Paolo II, 20025 Legnano, Italy
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Zhang H, Jiao L, Yang S, Li H, Jiang X, Feng J, Zou S, Xu Q, Gu J, Wang X, Wei B. Brain-computer interfaces: the innovative key to unlocking neurological conditions. Int J Surg 2024; 110:5745-5762. [PMID: 39166947 PMCID: PMC11392146 DOI: 10.1097/js9.0000000000002022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024]
Abstract
Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.
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Affiliation(s)
- Hongyu Zhang
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Le Jiao
- Department of Neurosurgery, The First Hospital of Qiqihar, Qiqihar, Heilongjiang Province
| | | | | | | | - Jing Feng
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Shuhuai Zou
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Qiang Xu
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Jianheng Gu
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Xuefeng Wang
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
| | - Baojian Wei
- School of Nursing, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, People's Republic of China
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邵 谢, 张 艺, 张 栋, 门 延, 王 子, 陈 晓, 谢 平. [Virtual reality-brain computer interface hand function enhancement rehabilitation system incorporating multi-sensory stimulation]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:656-663. [PMID: 39218590 PMCID: PMC11366477 DOI: 10.7507/1001-5515.202312055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 07/18/2024] [Indexed: 09/04/2024]
Abstract
Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.
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Affiliation(s)
- 谢宁 邵
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
- 河北省智能康复及神经调控重点实验室(河北秦皇岛 066000)Key Lab of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, Hebei 066000, P. R. China
| | - 艺滢 张
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
| | - 栋 张
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
- 河北省智能康复及神经调控重点实验室(河北秦皇岛 066000)Key Lab of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, Hebei 066000, P. R. China
| | - 延帝 门
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
- 河北省智能康复及神经调控重点实验室(河北秦皇岛 066000)Key Lab of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, Hebei 066000, P. R. China
| | - 子龙 王
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
| | - 晓玲 陈
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
- 河北省智能康复及神经调控重点实验室(河北秦皇岛 066000)Key Lab of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, Hebei 066000, P. R. China
| | - 平 谢
- 燕山大学 电气工程学院(河北秦皇岛 066000)College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066000, P. R. China
- 河北省智能康复及神经调控重点实验室(河北秦皇岛 066000)Key Lab of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, Hebei 066000, P. R. China
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Dillen A, Omidi M, Ghaffari F, Romain O, Vanderborght B, Roelands B, Nowé A, De Pauw K. User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface. SENSORS (BASEL, SWITZERLAND) 2024; 24:5253. [PMID: 39204948 PMCID: PMC11359122 DOI: 10.3390/s24165253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/02/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024]
Abstract
This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.
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Affiliation(s)
- Arnau Dillen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Equipes Traitement de l’Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l’Electronique et de ses Applications (ENSEA), Centre National de la Recherche Scientifique (CNRS), 95000 Cergy, France; (F.G.); (O.R.)
- Brussels Human Robotics Research Center (BruBotics), Vrije Universiteit Brussel, 1050 Brussels, Belgium; (M.O.); (B.V.)
| | - Mohsen Omidi
- Brussels Human Robotics Research Center (BruBotics), Vrije Universiteit Brussel, 1050 Brussels, Belgium; (M.O.); (B.V.)
- IMEC, 1050 Brussels, Belgium
| | - Fakhreddine Ghaffari
- Equipes Traitement de l’Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l’Electronique et de ses Applications (ENSEA), Centre National de la Recherche Scientifique (CNRS), 95000 Cergy, France; (F.G.); (O.R.)
| | - Olivier Romain
- Equipes Traitement de l’Information et Systèmes, UMR 8051, CY Cergy Paris Université, École Nationale Supérieure de l’Electronique et de ses Applications (ENSEA), Centre National de la Recherche Scientifique (CNRS), 95000 Cergy, France; (F.G.); (O.R.)
| | - Bram Vanderborght
- Brussels Human Robotics Research Center (BruBotics), Vrije Universiteit Brussel, 1050 Brussels, Belgium; (M.O.); (B.V.)
- IMEC, 1050 Brussels, Belgium
| | - Bart Roelands
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Brussels Human Robotics Research Center (BruBotics), Vrije Universiteit Brussel, 1050 Brussels, Belgium; (M.O.); (B.V.)
| | - Ann Nowé
- Artificial Intelligence Lab, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Kevin De Pauw
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, 1050 Brussels, Belgium
- Brussels Human Robotics Research Center (BruBotics), Vrije Universiteit Brussel, 1050 Brussels, Belgium; (M.O.); (B.V.)
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Blanco-Diaz CF, Guerrero-Mendez CD, de Andrade RM, Badue C, De Souza AF, Delisle-Rodriguez D, Bastos-Filho T. Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG. Med Biol Eng Comput 2024:10.1007/s11517-024-03147-3. [PMID: 39028484 DOI: 10.1007/s11517-024-03147-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 05/29/2024] [Indexed: 07/20/2024]
Abstract
Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.
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Affiliation(s)
| | | | | | - Claudine Badue
- Department of Informatics, Federal University of Espirito Santo, Vitoria, Brazil
| | | | - Denis Delisle-Rodriguez
- Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba, RN, Brazil
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil
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Gouret A, Le Bars S, Porssut T, Waszak F, Chokron S. Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review. Front Neurosci 2024; 18:1373377. [PMID: 38784094 PMCID: PMC11111994 DOI: 10.3389/fnins.2024.1373377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.
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Affiliation(s)
- Alix Gouret
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
- Research and Innovation Department, Capgemini Engineering, Paris, France
| | - Solène Le Bars
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
- Research and Innovation Department, Capgemini Engineering, Paris, France
| | - Thibault Porssut
- Research and Innovation Department, Capgemini Engineering, Paris, France
| | - Florian Waszak
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
| | - Sylvie Chokron
- Integrative Neuroscience and Cognition Center (INCC), CNRS, Université Paris Cité, Paris, France
- Research and Innovation Department, Capgemini Engineering, Paris, France
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Ferrero L, Soriano-Segura P, Navarro J, Jones O, Ortiz M, Iáñez E, Azorín JM, Contreras-Vidal JL. Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study. J Neuroeng Rehabil 2024; 21:48. [PMID: 38581031 PMCID: PMC10996198 DOI: 10.1186/s12984-024-01342-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/15/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. METHODS A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. RESULTS The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. CONCLUSION This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.
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Affiliation(s)
- Laura Ferrero
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain.
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain.
- International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain.
- NSF IUCRC BRAIN, University of Houston, Houston, USA.
- Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA.
| | - Paula Soriano-Segura
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain
| | - Jacobo Navarro
- NSF IUCRC BRAIN, University of Houston, Houston, USA
- International Affiliate NSF IUCRC BRAIN Site, Tecnológico de Monterrey, Monterrey, Mexico
- Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA
| | - Oscar Jones
- NSF IUCRC BRAIN, University of Houston, Houston, USA
- Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA
| | - Mario Ortiz
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain
| | - Eduardo Iáñez
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain
| | - José M Azorín
- Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, Elche, Spain
- International Affiliate NSF IUCRC BRAIN Site, Miguel Hernández University of Elche, Elche, Spain
- Valencian Graduate School and Research Network of Artificial Intelligence-valgrAI, Valencia, Spain
| | - José L Contreras-Vidal
- NSF IUCRC BRAIN, University of Houston, Houston, USA
- Non-Invasive Brain Machine Interface Systems, University of Houston, Houston, TX, USA
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Blanco-Díaz CF, Guerrero-Mendez CD, Delisle-Rodriguez D, Jaramillo-Isaza S, Ruiz-Olaya AF, Frizera-Neto A, Ferreira de Souza A, Bastos-Filho T. Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals. Biomed Phys Eng Express 2024; 10:035003. [PMID: 38417162 DOI: 10.1088/2057-1976/ad2e35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 02/28/2024] [Indexed: 03/01/2024]
Abstract
Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.
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Affiliation(s)
- Cristian Felipe Blanco-Díaz
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
- Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia
| | - Cristian David Guerrero-Mendez
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
- Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia
| | | | | | - Andrés Felipe Ruiz-Olaya
- Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia
| | - Anselmo Frizera-Neto
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
| | | | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
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Zhang J, Liu D, Chen W, Pei Z, Wang J. Boosting lower-limb motor imagery performance through an ensemble method for gait rehabilitation. Comput Biol Med 2024; 169:107910. [PMID: 38183703 DOI: 10.1016/j.compbiomed.2023.107910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/05/2023] [Accepted: 12/24/2023] [Indexed: 01/08/2024]
Abstract
Lower-limb exoskeletons have been used extensively in many rehabilitation applications to assist disabled people with their therapies. Brain-machine interfaces (BMIs) further provide effective and natural control schemes. However, the limited performance of brain signal decoding from lower-limb kinematics restricts the broad growth of both BMI and rehabilitation industry. To address these challenges, we propose an ensemble method for lower-limb motor imagery (MI) classification. The proposed model employs multiple techniques to boost performance, including deep and shallow parts. Traditional wavelet transformation followed by filter-bank common spatial pattern (CSP) employs neurophysiologically reasonable patterns, while multi-head self-attention (MSA) followed by temporal convolutional network (TCN) extracts deeper encoded generalized patterns. Experimental results in a customized lower-limb exoskeleton on 8 subjects in 3 consecutive sessions showed that the proposed method achieved 60.27% and 64.20% for three (MI of left leg, MI of right leg, and rest) and two classes (lower-limb MI vs. rest), respectively. Besides, the proposed model achieves improvements of up to 4% and 2% accuracy for the subject-specific and subject-independent modes compared to the current state-of-the-art (SOTA) techniques, respectively. Finally, feature analysis was conducted to show discriminative brain patterns in each MI task and sessions with different feedback modalities. The proposed models integrated in the brain-actuated lower-limb exoskeleton established a potential BMI for gait training and neuroprosthesis.
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Affiliation(s)
- Jing Zhang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China; Hangzhou Innovation Institute, Beihang University, Hangzhou, Zhejiang 310052, China.
| | - Dong Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
| | - Weihai Chen
- School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China.
| | - Zhongcai Pei
- Hangzhou Innovation Institute, Beihang University, Hangzhou, Zhejiang 310052, China.
| | - Jianhua Wang
- Hangzhou Innovation Institute, Beihang University, Hangzhou, Zhejiang 310052, China.
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