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Wang Z, Xiang L, Zhang R. P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:045116. [PMID: 38624364 DOI: 10.1063/5.0202770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.
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Affiliation(s)
- Zhongmin Wang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China
| | - Leihua Xiang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
| | - Rong Zhang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China
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Policastro P, Mesin L. Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications. Bioengineering (Basel) 2023; 10:1076. [PMID: 37760178 PMCID: PMC10525913 DOI: 10.3390/bioengineering10091076] [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: 07/29/2023] [Revised: 09/08/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
The inferior vena cava (IVC) is the largest vein in the body. It returns deoxygenated blood to the heart from the tissues placed under the diaphragm. The size and dynamics of the IVC depend on the blood volume and right atrial pressure, which are important indicators of a patient's hydration and reflect possible pathological conditions. Ultrasound (US) assessment of the IVC is a promising technique for evaluating these conditions, because it is fast, non-invasive, inexpensive, and without side effects. However, the standard M-mode approach for measuring IVC diameter is prone to errors due to the vein movements during respiration. B-mode US produces two-dimensional images that better capture the IVC shape and size. In this review, we discuss the pros and cons of current IVC segmentation techniques for B-mode longitudinal and transverse views. We also explored several scenarios where automated IVC segmentation could improve medical diagnosis and prognosis.
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Affiliation(s)
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy;
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Zhang Z, Chen G, Yang S. Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:6856-6866. [PMID: 34097619 DOI: 10.1109/tnnls.2021.3083710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.
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Fathi Y, Erfanian A. Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network. Front Neurosci 2022; 16:801818. [PMID: 35401098 PMCID: PMC8990134 DOI: 10.3389/fnins.2022.801818] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Abstract
To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats. The cats were trained to walk on a moving belt in a hindlimb-only condition, while their forelimbs were kept on the front body of the treadmill. The bilateral hindlimb joint angles were decoded using local field potential signals recorded using a microelectrode array implanted in the dorsal and lateral columns of both the left and right sides of the cat spinal cord. The results show that contralateral hindlimb kinematics can be decoded as accurately as ipsilateral kinematics. Interestingly, hindlimb kinematics of both legs can be accurately decoded from the lateral columns within one side of the spinal cord during hindlimb-only locomotion. The results indicated that there was no significant difference between the decoding performances obtained using neural signals recorded from the dorsal and lateral columns. The results of the time-frequency analysis show that event-related synchronization (ERS) and event-related desynchronization (ERD) patterns in all frequency bands could reveal the dynamics of the neural signals during movement. The onset and offset of the movement can be clearly identified by the ERD/ERS patterns. The results of the mutual information (MI) analysis showed that the theta frequency band contained significantly more limb kinematics information than the other frequency bands. Moreover, the theta power increased with a higher locomotion speed.
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Affiliation(s)
- Yaser Fathi
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
| | - Abbas Erfanian
- Department of Biomedical Engineering, School of Electrical Engineering, Iran Neural Technology Research Centre, Iran University of Science and Technology, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Abbas Erfanian,
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Zhang Z, Sun J, Chen T. A new dynamically convergent differential neural network for brain signal recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103130] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zheng M, Yang B. A deep neural network with subdomain adaptation for motor imagery brain-computer interface. Med Eng Phys 2021; 96:29-40. [PMID: 34565550 DOI: 10.1016/j.medengphy.2021.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND The nonstationarity problem of EEG is very serious, especially for spontaneous signals, which leads to the poor effect of machine learning related to spontaneous signals, especially in related tasks across time, which correspondingly limits the practical use of brain-computer interface (BCI). OBJECTIVE In this paper, we proposed a new transfer learning algorithm, which can utilize the labeled motor imagery (MI) EEG data at the previous time to achieve better classification accuracies for a small number of labeled EEG signals at the current time. METHODS We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The objective function of the adaptive layer was designed to minimize the Local Maximum Mean Discrepancy (LMMD) and the prediction error while minimizing the distance within each class (DWC) and maximizing the distance between classes within each domain (DBCWD). We verified the effectiveness of the proposed algorithm on two public datasets. RESULTS The classification accuracy of the proposed algorithm was higher than other comparison algorithms, and the paired t-test results also showed that the performance of the proposed algorithm was significantly different from that of other algorithms. The results of the confusion matrix and feature visualization showed the effectiveness of the proposed algorithm. CONCLUSION Experimental results showed that the proposed algorithm can achieve higher classification accuracy than other algorithms when there was only a small amount of labeled MI EEG data at the current time. It can be promising to be applied to the field of BCI.
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Affiliation(s)
- Minmin Zheng
- School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China; School of Mechanical and Electrical Engineering, Putian University, Fujian, China
| | - Banghua Yang
- School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China.
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Li S, Jin J, Daly I, Wang X, Lam HK, Cichocki A. Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method. J Neurosci Methods 2021; 362:109300. [PMID: 34343575 DOI: 10.1016/j.jneumeth.2021.109300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/14/2021] [Accepted: 07/29/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability. NEW METHODS In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention. RESULTS The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance. COMPARISON WITH EXISTING METHODS The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms. CONCLUSIONS The proposed MSFF method is able to improve the performance of P300-based BCIs.
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Affiliation(s)
- Shurui Li
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Hak-Keung Lam
- Department of Engineering, King's College London, London WC2R 2LS, UK
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia; Systems Research Institute PAS, Warsaw, Poland; Nicolaus Copernicus University (UMK), Torun, Poland
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Onishi A. Convolutional Neural Network Transfer Learning Applied to the Affective Auditory P300-Based BCI. JOURNAL OF ROBOTICS AND MECHATRONICS 2020. [DOI: 10.20965/jrm.2020.p0731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain-computer interface (BCI) enables us to interact with the external world via electroencephalography (EEG) signals. Recently, deep learning methods have been applied to the BCI to reduce the time required for recording training data. However, more evidence is required due to lack of comparison. To reveal more evidence, this study proposed a deep learning method named time-wise convolutional neural network (TWCNN), which was applied to a BCI dataset. In the evaluation, EEG data from a subject was classified utilizing previously recorded EEG data from other subjects. As a result, TWCNN showed the highest accuracy, which was significantly higher than the typically used classifier. The results suggest that the deep learning method may be useful to reduce the recording time of training data.
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Song Y, Cai S, Yang L, Li G, Wu W, Xie L. A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot. Front Neurorobot 2020; 14:32. [PMID: 32754025 PMCID: PMC7366778 DOI: 10.3389/fnbot.2020.00032] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 05/06/2020] [Indexed: 12/23/2022] Open
Abstract
Background and Objective: Electroencephalography (EEG) can be used to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into this but still not enough for online use. To improve the practicality, this study aims to propose an efficient control method based on P300, a special EEG component. Moreover, we have developed an upper-limb assist robot system with the method for verification and hope to really help paralyzed people. Methods: We chose P300, which is highly available and easily accepted to obtain the user's intention. Preprocessing and spatial enhancement were firstly implemented on raw EEG data. Then, three approaches– linear discriminant analysis, support vector machine, and multilayer perceptron –were compared in detail to accomplish an efficient P300 detector, whose output was employed as a command to control the assist robot. Results: The method we proposed achieved an accuracy of 94.43% in the offline test with the data from eight participants. It showed sufficient reliability and robustness with an accuracy of 80.83% and an information transfer rate of 15.42 in the online test. Furthermore, the extended test showed remarkable generalizability of this method that can be used in more complex application scenarios. Conclusion: From the results, we can see that the proposed method has great potential for helping paralyzed people easily control an assist robot to do numbers of things.
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Affiliation(s)
- Yonghao Song
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Siqi Cai
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Lie Yang
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Guofeng Li
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Weifeng Wu
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Longhan Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
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