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Chen J, Xia Y, Zhou X, Vidal Rosas E, Thomas A, Loureiro R, Cooper RJ, Carlson T, Zhao H. fNIRS-EEG BCIs for Motor Rehabilitation: A Review. Bioengineering (Basel) 2023; 10:1393. [PMID: 38135985 PMCID: PMC10740927 DOI: 10.3390/bioengineering10121393] [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: 09/28/2023] [Revised: 11/26/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.
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Affiliation(s)
- Jianan Chen
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
| | - Yunjia Xia
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
| | - Xinkai Zhou
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
| | - Ernesto Vidal Rosas
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
- Digital Health and Biomedical Engineering, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Alexander Thomas
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
- Aspire CREATe, Department of Orthopaedics & Musculoskeletal Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (R.L.); (T.C.)
| | - Rui Loureiro
- Aspire CREATe, Department of Orthopaedics & Musculoskeletal Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (R.L.); (T.C.)
| | - Robert J. Cooper
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
| | - Tom Carlson
- Aspire CREATe, Department of Orthopaedics & Musculoskeletal Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (R.L.); (T.C.)
| | - Hubin Zhao
- HUB of Intelligent Neuro-engineering (HUBIN), Aspire CREATe, IOMS, Division of Surgery and Interventional Science, University College London (UCL), Stanmore, London HA7 4LP, UK; (J.C.); (Y.X.); (X.Z.); (A.T.)
- DOT-HUB, Department of Medical Physics & Biomedical Engineering, University College London (UCL), London WC1E 6BT, UK; (E.V.R.); (R.J.C.)
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Zafar A, Hussain SJ, Ali MU, Lee SW. Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073714. [PMID: 37050774 PMCID: PMC10098559 DOI: 10.3390/s23073714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 06/01/2023]
Abstract
In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.
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Affiliation(s)
- Amad Zafar
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Shaik Javeed Hussain
- Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman
| | - Muhammad Umair Ali
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung Won Lee
- Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Zafar A, Dad Kallu K, Atif Yaqub M, Ali MU, Hyuk Byun J, Yoon M, Su Kim K. A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8812844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy
. However, the results of the ReliefF algorithm are more stable and significant.
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EEG-Based Empathic Safe Cobot. MACHINES 2022. [DOI: 10.3390/machines10080603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalographic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment involved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the presence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was acquired. The result was that a spike in the subject’s EEG signal was observed in the presence of uncomfortable movement. The questionnaires were distributed to the subjects, and confirmed the results of the EEG signal measurement. In a controlled laboratory setting, all experiments were found to be statistically significant. In the first experiment, the peak EEG signal measured just after the activating event was greater than the resting EEG signal (p < 10−3). In the second experiment, the peak EEG signal measured just after the uncomfortable movement of the cobot was greater than the EEG signal measured under conditions of comfortable movement of the cobot (p < 10−3). In conclusion, within the isolated and constrained experimental environment, the results were satisfactory.
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Silva EMGS, Holanda LJ, Coutinho GKB, Andrade FS, Nascimento GIS, Nagem DAP, Valentim RADM, Lindquist AR. Effects of Active Upper Limb Orthoses Using Brain-Machine Interfaces for Rehabilitation of Patients With Neurological Disorders: Protocol for a Systematic Review and Meta-Analysis. Front Neurosci 2021; 15:661494. [PMID: 34248477 PMCID: PMC8264786 DOI: 10.3389/fnins.2021.661494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/04/2021] [Indexed: 12/01/2022] Open
Abstract
Introduction: The field of brain–machine interfaces (BMI) for upper limb (UL) orthoses is growing exponentially due to improvements in motor performance, quality of life, and functionality of people with neurological diseases. Considering this, we planned a systematic review to investigate the effects of BMI-controlled UL orthoses for rehabilitation of patients with neurological disorders. Methods: This systematic review and meta-analysis protocol was elaborated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P 2015) and Cochrane Handbook for Systematic Reviews of Interventions. A search will be conducted on Pubmed, IEEE Xplore Digital Library, Medline, and Web of Science databases without language and year restrictions, and Patents Scope, Patentlens, and Google Patents websites in English, Spanish, French, German, and Portuguese between 2011 and 2021. Two independent reviewers will include randomized controlled trials and quasi-experimental studies using BMI-controlled active UL orthoses to improve human movement. Studies must contain participants aged >18 years, diagnosed with neurological disorders, and with impaired UL movement. Three independent reviewers will conduct the same procedure for patents. Evidence quality and risk of bias will be evaluated following the Cochrane collaboration by two review authors. Meta-analysis will be conducted in case of homogeneity between groups. Otherwise, a narrative synthesis will be performed. Data will be inserted into a table containing physical description, UL orthoses control system, and effect of BMI-controlled orthoses. Discussion: BMI-controlled orthoses can assist individuals in several routine activities and provide functional independence and sense of overcoming limitations imposed by the underlying disease. These benefits will also be associated with orthoses descriptions, safety, portability, adverse events, and tools used to assess UL motor performance in patients with neurological disorders. PROSPERO Registration Number: CRD42020182195.
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Affiliation(s)
- Emília M G S Silva
- Laboratory of Intervention and Analysis of Movement, Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.,Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ledycnarf J Holanda
- Laboratory of Intervention and Analysis of Movement, Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.,Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gustavo K B Coutinho
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Fernanda S Andrade
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.,Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Gabriel I S Nascimento
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Danilo A P Nagem
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.,Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ricardo A de M Valentim
- Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil.,Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Ana Raquel Lindquist
- Laboratory of Intervention and Analysis of Movement, Department of Physical Therapy, Federal University of Rio Grande do Norte, Natal, Brazil.,Laboratory of Technological Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil
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Evaluation of Commercial Ropes Applied as Artificial Tendons in Robotic Rehabilitation Orthoses. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10030920] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study aims to present the design, selection and testing of commercial ropes (artificial tendons) used on robotic orthosis to perform the hand movements for stroke individuals over upper limb rehabilitation. It was determined the load applied in the rope would through direct measurements performed on four individuals after stroke using a bulb dynamometer. A tensile strength test was performed using eight commercial ropes in order to evaluate the maximum breaking force and select the most suitable to be used in this application. Finally, a pilot test was performed with a user of the device to ratify the effectiveness of the rope. The load on the cable was 12.38 kgf (121.4 N) in the stroke-affected hand, which is the maximum tensile force that the rope must to supports. Paragliding rope (DuPont™ Kevlar ® ) supporting a load of 250 N at a strain of 37 mm was selected. The clinical test proved the effectiveness of the rope, supporting the requested efforts, without presenting permanent deformation, effectively performing the participant’s finger opening.
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Heydari Beni N, Foodeh R, Shalchyan V, Daliri MR. Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression? AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2020; 43:10.1007/s13246-019-00833-7. [PMID: 31898242 DOI: 10.1007/s13246-019-00833-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 12/09/2019] [Indexed: 11/30/2022]
Abstract
The development of brain-computer interface (BCI) systems is an important approach in brain studies. Control of communication devices and prostheses in real-world scenarios requires complex movement parameters. Decoding a variety of neural signals captured by micro-wire arrays is a potential applicant for extracting movement-related information. The present work was conducted to compare the functionality of partial least square (PLS) regression and Kalman filter to predict the force parameter from local field potential (LFP) signals of the primary motor cortex (M1). The signals were recorded using a 16-channel micro-wire array from the forelimb-related area of the M1 of three rats performing a behavioral task in which the force signal of the rat's forelimb paw was generated. Our results show that PLS regression and Kalman filters with the mean performance of 0.75 and 0.72 in terms of the correlation coefficient (CC) and 0.37 and 0.48 in terms of normalized mean square error (NMSE), respectively, are effective methods for decoding the force parameter from LFPs. Kalman filter underperforms PLS both in performance and speed. Although adding nonlinearity to the Kalman filter results in equally accurate CC performance as PLS, it has even more computational cost. Therefore, it is inferred that nonlinear methods do not necessarily have better functionality than linear ones and PLS, as a simple fast linear method could be an effectively applicable regression technique for BCIs.
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Affiliation(s)
- Nargess Heydari Beni
- Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran
- Engineering Bionics Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
| | - Reza Foodeh
- Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran
| | - Vahid Shalchyan
- Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran
| | - Mohammad Reza Daliri
- Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran.
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Design and Analysis of a Chinese Medicine Based Humanoid Robotic Arm Massage System. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9204294] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
This paper presents a humanoid robotic arm massage system with an aim toward satisfying the clinical requirements of pain relief on the waist and legs of older patients during Chinese medicinal massage. On the basis of an in-depth analysis regarding the characteristics of arm joints of the human body and Chinese medicinal massage theory, a humanoid robotic arm massage system was designed by adapting a bottom to top modular method. The combined finite element and kinematic analysis led to an improved performance according to repeated positioning accuracy, massage strength accuracy, and massage effect. The developed humanoid robotic arm was characterized by a compact structure, high precision, light quality, and good stiffness, achieving a good bearing capacity. Due to the PID controller, the numerical simulations and experimental results provided valuable insight into the development of Chinese medicinal massage robots and massage treatments for patients who suffer from lumbar muscle strain.
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