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Venu K, Natesan P. Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task. BIOMED ENG-BIOMED TE 2024; 69:125-140. [PMID: 37935217 DOI: 10.1515/bmt-2023-0407] [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: 01/03/2023] [Accepted: 09/30/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task. METHODS The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics. RESULTS A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST. CONCLUSIONS The proposed method achieved effective classification performance in terms of performance measures.
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Affiliation(s)
- K Venu
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
| | - P Natesan
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
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Fu J, Chen S, Shu X, Lin Y, Jiang Z, Wei D, Gao J, Jia J. Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study. Front Neurosci 2023; 17:1146146. [PMID: 37250399 PMCID: PMC10213744 DOI: 10.3389/fnins.2023.1146146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023] Open
Abstract
Background Brain-computer interfaces (BCIs) have been proven to be effective for hand motor recovery after stroke. Facing kinds of dysfunction of the paretic hand, the motor task of BCIs for hand rehabilitation is relatively single, and the operation of many BCI devices is complex for clinical use. Therefore, we proposed a functional-oriented, portable BCI equipment and explored the efficiency of hand motor recovery after a stroke. Materials and methods Stroke patients were randomly assigned to the BCI group and the control group. The BCI group received BCI-based grasp/open motor training, while the control group received task-oriented guidance training. Both groups received 20 sessions of motor training in 4 weeks, and each session lasted for 30 min. The Fugl-Meyer assessment of the upper limb (FMA-UE) was applied for the assessment of rehabilitation outcomes, and the EEG signals were obtained for processing. Results The progress of FMA-UE between the BCI group [10.50 (5.75, 16.50)] and the control group [5.00 (4.00, 8.00)] was significantly different (Z = -2.834, P = 0.005). Meanwhile, the FMA-UE of both groups improved significantly (P < 0.001). A total of 24 patients in the BCI group achieved the minimal clinically important difference (MCID) of FMA-UE with an effective rate of 80%, and 16 in the control group achieved the MCID, with an effective rate of 51.6%. The lateral index of the open task in the BCI group was significantly decreased (Z = -2.704, P = 0.007). The average BCI accuracy for 24 stroke patients in 20 sessions was 70.7%, which was improved by 5.0% in the final session compared with the first session. Conclusion Targeted hand movement and two motor task modes, namely grasp and open, to be applied in a BCI design may be suitable in stroke patients with hand dysfunction. The functional-oriented, portable BCI training can promote hand recovery after a stroke, and it is expected to be widely used in clinical practice. The lateral index change of inter-hemispheric balance may be the mechanism of motor recovery. Trial registration number ChiCTR2100044492.
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Affiliation(s)
- Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yifang Lin
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zewu Jiang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Dongshuai Wei
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiajia Gao
- Department of Rehabilitation Medicine, Shanghai No. 3 Rehabilitation Hospital, Shanghai, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
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Fu J, Jiang Z, Shu X, Chen S, Jia J. Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study. Biomed Eng Online 2023; 22:36. [PMID: 37061673 PMCID: PMC10105926 DOI: 10.1186/s12938-023-01091-1] [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: 10/04/2022] [Accepted: 03/02/2023] [Indexed: 04/17/2023] Open
Abstract
BACKGROUND AND AIMS Brain-computer interfaces (BCIs) are emerging as a promising tool for upper limb recovery after stroke, and motor tasks are an essential part of BCIs for patient training and control of rehabilitative/assistive BCIs. However, the correlation between brain activation with different levels of motor impairment and motor tasks in BCIs is still not so clear. Thus, we aim to compare the brain activation of different levels of motor impairment in performing the hand grasping and opening tasks in BCIs. METHODS We instructed stroke patients to perform motor attempts (MA) to grasp and open the affected hand for 30 trials, respectively. During this period, they underwent EEG acquisition and BCIs accuracy recordings. They also received detailed history records and behavioral scale assessments (the Fugl-Meyer assessment of upper limb, FMA-UE). RESULTS The FMA-UE was negatively correlated with the event-related desynchronization (ERD) of the affected hemisphere during open MA (R = - 0.423, P = 0.009) but not with grasp MA (R = - 0.058, P = 0.733). Then we divided the stroke patients into group 1 (Brunnstrom recovery stages between I to II, n = 19) and group 2 (Brunnstrom recovery stages between III to VI, n = 23). No difference during the grasping task (t = 0.091, P = 0.928), but a significant difference during the open task (t = 2.156, P = 0.037) was found between the two groups on the affected hemisphere. No significant difference was found in the unaffected hemisphere. CONCLUSIONS The study indicated that brain activation is positively correlated with the hand function of stroke in open-hand tasks. In the grasping task, the patients in the different groups have a similar brain response, while in the open task, mildly injured patients have more brain activation in open the hand than the poor hand function patients.
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Affiliation(s)
- Jianghong Fu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China
| | - ZeWu Jiang
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China
| | - Xiaokang Shu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China.
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, 12 Mid-Wulumuqi Road, Jing'an District, Shanghai, 200040, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Shanghai, China.
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Rezaei E, Shalbaf A. Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in Electroencephalogram Signal. Basic Clin Neurosci 2023; 14:213-224. [PMID: 38107527 PMCID: PMC10719976 DOI: 10.32598/bcn.2021.2034.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/18/2021] [Accepted: 09/18/2021] [Indexed: 12/19/2023] Open
Abstract
Introduction The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. Methods TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. Conclusion The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals. Highlights Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task. Plain Language Summary In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.
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Affiliation(s)
- Erfan Rezaei
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Du Q, Luo J, Cheng Q, Wang Y, Guo S. Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities. Front Neurosci 2022; 16:935827. [PMID: 36267238 PMCID: PMC9577243 DOI: 10.3389/fnins.2022.935827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Stroke patients often suffer from hand dysfunction or loss of tactile perception, which in turn interferes with hand rehabilitation. Tactile-enhanced multi-sensory feedback rehabilitation is an approach worth considering, but its effectiveness has not been well studied. By using functional near-infrared spectroscopy (fNIRS) to analyze the causal activity patterns in the sensorimotor cortex, the present study aims to investigate the cortical hemodynamic effects of hand rehabilitation training when tactile stimulation is applied, and to provide a basis for rehabilitation program development. Methods A vibrotactile enhanced pneumatically actuated hand rehabilitation device was tested on the less-preferred hand of 14 healthy right-handed subjects. The training tasks consisted of move hand and observe video (MO), move hand and vibration stimulation (MV), move hand, observe video, and vibration stimulation (MOV), and a contrast resting task. Region of interest (ROI), a laterality index (LI), and causal brain network analysis methods were used to explore the brain’s cortical blood flow response to a multi-sensory feedback rehabilitation task from multiple perspectives. Results (1) A more pronounced contralateral activation in the right-brain region occurred under the MOV stimulation. Rehabilitation tasks containing vibrotactile enhancement (MV and MOV) had significantly more oxyhemoglobin than the MO task at 5 s after the task starts, indicating faster contralateral activation in sensorimotor brain regions. (2) Five significant lateralized channel connections were generated under the MV and MOV tasks (p < 0.05), one significant lateralized channel connection was generated by the MO task, and the Rest were not, showing that MV and MOV caused stronger lateralization activation. (3) We investigated all thresholds of granger causality (GC) resulting in consistent relative numbers of effect connections. MV elicited stronger causal interactions between the left and right cerebral hemispheres, and at the GC threshold of 0.4, there were 13 causal network connection pairs for MV, 7 for MO, and 9 for MOV. Conclusion Vibrotactile cutaneous stimulation as a tactile enhancement can produce a stronger stimulation of the brain’s sensorimotor brain areas, promoting the establishment of neural pathways, and causing a richer effect between the left and right cerebral hemispheres. The combination of kinesthetic, vibrotactile, and visual stimulation can achieve a more prominent training efficiency from the perspective of functional cerebral hemodynamics.
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Affiliation(s)
- Qiang Du
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
| | - Jingjing Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
- Jihua Laboratory, Foshan, China
- *Correspondence: Jingjing Luo,
| | - Qiying Cheng
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
| | - Youhao Wang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
| | - Shijie Guo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of AI and Robotics, Shanghai, China
- Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai, China
- Department of the State Key Laboratory of Reliability and Intelligence of Electrical Equipment and the Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, China
- Shijie Guo,
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Wang J, Chen YH, Yang J, Sawan M. Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern. BIOSENSORS 2022; 12:bios12060384. [PMID: 35735532 PMCID: PMC9221354 DOI: 10.3390/bios12060384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 11/16/2022]
Abstract
To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.
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Affiliation(s)
- Jiachen Wang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
| | - Yun-Hsuan Chen
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
- Correspondence: (Y.-H.C.); (M.S.)
| | - Jie Yang
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Mohamad Sawan
- Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou 310024, China; (J.W.); (J.Y.)
- Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China
- Correspondence: (Y.-H.C.); (M.S.)
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Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9935192. [PMID: 35252458 PMCID: PMC8896931 DOI: 10.1155/2022/9935192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions. This technique has been widely used in the rehabilitation of stroke patients in recent years and appears to promote motor function recovery after stroke. At present, the application of BCI in poststroke cognitive impairment is increasing, which is a common complication that also affects the rehabilitation process. This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.
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Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M. Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network. SENSORS (BASEL, SWITZERLAND) 2021; 21:6570. [PMID: 34640888 PMCID: PMC8512774 DOI: 10.3390/s21196570] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/24/2022]
Abstract
Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
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Affiliation(s)
- Muhammad Ahsan Awais
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Mohd Zuki Yusoff
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Danish M. Khan
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
- Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan
| | - Norashikin Yahya
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Nidal Kamel
- Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.Z.Y.); (D.M.K.); (N.Y.); (N.K.)
| | - Mansoor Ebrahim
- Faculty of Engineering, Sciences, and Technology, Iqra University, Karachi 75500, Pakistan;
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Effects of visual-motor illusion on functional connectivity during motor imagery. Exp Brain Res 2021; 239:2261-2271. [PMID: 34081177 DOI: 10.1007/s00221-021-06136-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/05/2021] [Indexed: 10/21/2022]
Abstract
This study aimed to verify whether visual-motor illusion changes the functional connectivity during kinesthetic motor imagery and the vividness of kinesthetic motor imagery. Twelve right-handed healthy adults participated in this study. All participants randomly performed both the illusion and observation conditions in 20 min, respectively. Illusion condition was induced kinesthetic illusion by viewing own finger movement video. Observation condition was observed own finger movement video. Before and after each condition, the brain activity of kinesthetic motor imagery was measured using functional near-infrared spectroscopy. The measure of brain activity under kinesthetic motor imagery was executed in five sets using block design. Under the kinesthetic motor imagery, participants were asked to imagine the movement of their right finger. Functional connectivity was analyzed during the kinesthetic motor imagery. In addition, after performing the task under kinesthetic motor imagery, the vividness of the kinesthetic motor imagery was measured using a visual analog scale. Furthermore, after each condition, the degree of kinesthetic illusion and sense of body ownership measured based on a seven-point Likert scale. Our results indicated that the functional connectivity during kinesthetic motor imagery was changed in the frontal-parietal network of the right hemisphere. The vividness of the kinesthetic motor imagery was significantly higher with the illusion condition compared with the observation condition. The degree of kinesthetic illusion and sense of body ownership were significantly higher with the illusion condition compared with the observation condition. In conclusion, the visual-motor illusion changes the functional connectivity during kinesthetic motor imagery and influences the vividness of kinesthetic motor imagery. The visual-motor illusion provides evidence that it improves motor imagery ability. VMI may be used in patients with impaired motor imagery.
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Sosnik R, Li Z. Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles. J Neural Eng 2021; 18. [PMID: 33752186 DOI: 10.1088/1741-2552/abf0d7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 03/22/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Growing evidence suggests that EEG electrode (sensor) potential time series (PTS) of slow cortical potentials (SCPs) hold motor neural correlates that can be used for motion trajectory prediction (MTP), commonly by multiple linear regression (mLR). It is not yet known whether arm-joint trajectories can be reliably decoded from current sources, computed from sensor data, from which brain areas they can be decoded and using which neural features. APPROACH In this study, the PTS of 44 sensors were fed into sLORETA source localization software to compute current source activity in 30 regions of interest (ROIs) found in a recent meta-analysis to be engaged in action execution, motor imagery and motor preparation. The current sources PTS and band-power time series (BTS) in several frequency bands and time lags were used to predict actual and imagined trajectories in 3D space of the three velocity components of the hand, elbow and shoulder of nine subjects using an mLR model. MAIN RESULTS For all arm joints and movement types, current source SCPs PTS contributed most to trajectory reconstruction with time lags 150ms, 116ms and 84ms providing the highest contribution, and current source BTS in any of the tested frequency bands was not informative. Person's correlation coefficient (r) averaged across movement types, arm joints and velocity components using source data was slightly lower than using sensor data (r=0.25 and r=0.28, respectively). For each ROI, the three current source dipoles had different contribution to the reconstruction of each of the three velocity components. SIGNIFICANCE Overall, our results demonstrate the feasibility of predicting of actual and imagined 3D trajectories of all arm joints from current sources, computed from scalp EEG. These findings may be used by developers of a future BCI as a validated set of contributing ROIs.
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Affiliation(s)
- Ronen Sosnik
- Electrical, Electronics and Communication Engineering, Holon Institute of Technology, 52 Golomb St., Holon, 5810201, ISRAEL
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Yingdong Building, Xinjiekouwai Street 19, Beijing Haidian, Beijing, 100875, CHINA
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Pei Y, Luo Z, Yan Y, Yan H, Jiang J, Li W, Xie L, Yin E. Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG. Front Hum Neurosci 2021; 15:645952. [PMID: 33776673 PMCID: PMC7990774 DOI: 10.3389/fnhum.2021.645952] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.
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Affiliation(s)
- Yu Pei
- School of Software, Beihang University, Beijing, China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
| | - Zhiguo Luo
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China.,Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China
| | - Ye Yan
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China.,Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China
| | - Huijiong Yan
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China.,Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China
| | - Jing Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Weiguo Li
- School of Software, Beihang University, Beijing, China
| | - Liang Xie
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China.,Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China
| | - Erwei Yin
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China.,Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China
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12
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Wen D, Liang B, Zhou Y, Chen H, Jung TP. The Current Research of Combining Multi-Modal Brain-Computer Interfaces With Virtual Reality. IEEE J Biomed Health Inform 2020; 25:3278-3287. [PMID: 33373308 DOI: 10.1109/jbhi.2020.3047836] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Combing brain-computer interfaces (BCI) and virtual reality (VR) is a novel technique in the field of medical rehabilitation and game entertainment. However, the limitations of BCI such as a limited number of action commands and low accuracy hinder the widespread use of BCI-VR. Recent studies have used hybrid BCIs that combine multiple BCI paradigms and/or the multi-modal biosensors to alleviate these issues, which may become the mainstream of BCIs in the future. The main purpose of this review is to discuss the current status of multi-modal BCI-VR. This study first reviewed the development of the BCI-VR, and explored the advantages and disadvantages of incorporating eye tracking, motor capture, and myoelectric sensing into the BCI-VR system. Then, this study discussed the development trend of the multi-modal BCI-VR, hoping to provide a pathway for further research in this field.
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13
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Hoshino T, Oguchi K, Inoue K, Hoshino A, Hoshiyama M. Relationship between lower limb function and functional connectivity assessed by EEG among motor-related areas after stroke. Top Stroke Rehabil 2020; 28:614-623. [PMID: 33351724 DOI: 10.1080/10749357.2020.1864986] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background: Neural connectivity in brain has been known as indicators for neural function and recovery of brain. Although previous studies reported that neural connectivity predicted the recovery of upper limb function after stroke, the relationship between neural connectivity and lower limb function has not been clear.Objectives: To clarify whether functional connectivity (FC) assessed by electroencephalographiy (EEG) with five electrodes placed on motor-related areas could be related to the functional motor recovery of the lower limbs in patients after stroke.Methods: Twenty-four patients with stroke during the recovery phase were recruited. Motor function of the lower limbs was assessed using Fugl-Meyer Assessment lower limb section (FMAL). EEG signals were recorded by five electrodes (C3, C4, FC3, FC4, and FCz) at rest and during ankle movement. Amplitude envelope correlations, as values for FC, were calculated in α (8-12 Hz), β (13-30 Hz), low-β (13-19 Hz), and high-β (20-30 Hz) frequency bands. The predictive regression equation of the FMAL score in the eighth week after stroke (8 W) was created by FCs in the fourth week (4 W).Results: The higher intra-hemispheric FC in both hemispheres in the resting state and during the ankle movement at 4 W was related to a higher lower limb function at 8 W. Additionally, the higher inter-hemispheric FC between M1 on both sides during the ankle movement was related to a higher function recovery.Conclusions: The intra- and inter-hemispheric FC among motor-related areas at 4 W after stroke might be related to the functional recovery of the lower limbs at 8 W.
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Affiliation(s)
- Takashi Hoshino
- Department of Rehabilitation, Kariya Toyota General Hospital, Kariya, Japan.,Department of Rehabilitation Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Kazuyo Oguchi
- Department of Rehabilitation, Kariya Toyota General Hospital, Kariya, Japan
| | - Kenji Inoue
- Department of Clinical Laboratory Pathology, Kariya Toyota General Hospital, Kariya, Japan
| | - Aiko Hoshino
- Department of Rehabilitation Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Minoru Hoshiyama
- Department of Rehabilitation Sciences, Graduate School of Medicine, Nagoya University, Nagoya, Japan.,Brain & Mind Research Center, Nagoya University, Nagoya, Japan
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14
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Wei W, Qiu S, Ma X, Li D, Wang B, He H. Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2344-2355. [DOI: 10.1109/tnsre.2020.3023761] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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15
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The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8825197. [PMID: 33082776 PMCID: PMC7559784 DOI: 10.1155/2020/8825197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 07/30/2020] [Accepted: 09/02/2020] [Indexed: 11/18/2022]
Abstract
Perceptual organization is an important part of visual and auditory information processing. In the case of visual occlusion, whether the loss of information in images could be recovered and thus perceptually closed affects object recognition. In particular, many elderly subjects have defects in object recognition ability, which may be closely related to the abnormalities of perceptual functions. This phenomenon even can be observed in the early stage of dementia. Therefore, studying the neural mechanism of perceptual closure and its relationship with sensory and cognitive processing is important for understanding how the human brain recognizes objects, inspiring the development of neuromorphic intelligent algorithms of object recognition. In this study, a new experiment was designed to explore the realistic process of perceptual closure under occlusion and intact conditions of faces and building. The analysis of the differences in ERP components P1, N1, and Ncl indicated that the subjective awareness of perceptual closure mainly occurs in Ncl, but incomplete information has been processed and showed different manners compared to complete stimuli in N170 for facial materials. Although occluded, faces, but not buildings, still maintain the specificity of perceptual processing. The Ncl by faces and buildings did not show significant differences in both amplitude and latency, suggesting a “completing” process regardless of categorical features.
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16
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Jin J, Liu C, Daly I, Miao Y, Li S, Wang X, Cichocki A. Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2153-2163. [PMID: 32870796 DOI: 10.1109/tnsre.2020.3020975] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).
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17
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Belkacem AN, Jamil N, Palmer JA, Ouhbi S, Chen C. Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients. Front Neurosci 2020; 14:692. [PMID: 32694979 PMCID: PMC7339951 DOI: 10.3389/fnins.2020.00692] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 06/08/2020] [Indexed: 02/01/2023] Open
Abstract
All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.
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Affiliation(s)
- Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Nuraini Jamil
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Jason A. Palmer
- Department of Neurological Diagnosis and Restoration, Osaka University, Suita, Japan
| | - Sofia Ouhbi
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
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18
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Srisupornkornkool K, Sornkaew K, Chatkanjanakool K, Ampairattana C, Pongtasom P, Somthavil S, Boonyarom O, Yuenyongchaiwat K, Pongpanit K. Electromyography features during physical and imagined standing up in healthy young adults, Phitsanulok, Thailand. JOURNAL OF HEALTH RESEARCH 2020. [DOI: 10.1108/jhr-08-2019-0175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
PurposeTo compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.Design/methodology/approachTwenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.FindingsThe onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.Originality/valueMuscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.
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EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:6968713. [PMID: 32399166 PMCID: PMC7201509 DOI: 10.1155/2020/6968713] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/29/2019] [Accepted: 11/29/2019] [Indexed: 01/24/2023]
Abstract
The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people's lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.
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20
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Shao L, Zhang L, Li S, Zhang P. Design and Quantitative Analysis of Cancer Detection System Based on Fluorescence Immune Analysis. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:1672940. [PMID: 31934322 PMCID: PMC6942821 DOI: 10.1155/2019/1672940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 11/04/2019] [Accepted: 11/20/2019] [Indexed: 11/18/2022]
Abstract
Human blood is an important medical detection index. With the development in clinical medical detection instruments and detection technology, the requirements for detection accuracy and efficiency have been gradually improved. Fluorescent immunochromatography is a new detection technique. It has the characteristics of high efficiency, convenience, no pollution, and wide detection range. Human blood can be detected quickly using fluorescent immunochromatography. At present, it has received great attention from the field of clinical testing. In this paper, a set of fluorescent immunochromatographic analyzer has been designed. It is mainly based on the principle of fluorescence immunochromatography. A new method of signal analysis and system design for fluorescent immunochromatography analyzer is proposed. By using the improved threshold function denoising algorithm, the quantitative detection of fluorescent immunochromatographic strip is realized. The concentration of pathogenic factors (cancer cells) in human serum can be measured conveniently and accurately. The system integrates many peripheral modules, including fluorescence signal acquisition, fluorescence signal processing, quantitative curve fitting, and test results. In this paper, the quantitative detection experiments of the system are carried out in three aspects: linearity, repeatability, and sensitivity. The experimental results show that the linear correlation coefficient is up to 0.9976, and the limit of detection is up to 0.05 ng/ml. The requirements of the system are satisfied. The system performance is good, and the quantitative result is accurate. Therefore, the establishment of a fluorescence analysis system is of great significance.
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Affiliation(s)
- Lei Shao
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China
| | - Longyu Zhang
- Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China
| | - Shilin Li
- Tianjin Xinuo Biomedicine Co., Ltd., Tianjin, China
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