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Moraes CPA, Dos Santos LH, Fantinato DG, Neves A, Adali T. Independent Vector Analysis for Feature Extraction in Motor Imagery Classification. SENSORS (BASEL, SWITZERLAND) 2024; 24:5428. [PMID: 39205122 PMCID: PMC11359939 DOI: 10.3390/s24165428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 09/04/2024]
Abstract
Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.
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Affiliation(s)
- Caroline Pires Alavez Moraes
- Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil
| | - Lucas Heck Dos Santos
- Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil
| | - Denis Gustavo Fantinato
- Department of Computer Engineering and Automation (DCA), Universidade Estadual de Campinas (UNICAMP), Campinas 13083-852, SP, Brazil
| | - Aline Neves
- Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo André 09280-560, SP, Brazil
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County (UMBC), Baltimore, MD 21250, USA
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Zhang K, Hu X. Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization. Comput Biol Med 2024; 178:108700. [PMID: 38852400 DOI: 10.1016/j.compbiomed.2024.108700] [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: 01/29/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 06/11/2024]
Abstract
Event-related potentials (ERPs) can quantify brain responses to reveal the neural mechanisms of sensory perception. However, ERPs often reflect nonlinear mixture responses to multiple sources of sensory stimuli, and an accurate separation of the response to each stimulus remains a challenge. This study aimed to separate the ERP into nonlinearly mixed source components specific to individual stimuli. We developed an unsupervised learning method based on clustering of manifold structures of mixture signals combined with channel optimization for signal source reconstruction using non-negative matrix factorization (NMF). Specifically, we first implemented manifold learning based on Local Tangent Space Alignment (LTSA) to extract the spatial manifold structure of multi-resolution sub-signals separated via wavelet packet transform. We then used fuzzy entropy to extract the dynamical process of the manifold structures and performed a k-means clustering to separate different sources. Lastly, we used NMF to obtain the optimal contributions of multiple channels to ensure accurate source reconstructions. We evaluated our developed approach using a simulated ERP dataset with known ground truth of two components of ERP mixture signals. Our results show that the correlation coefficient between the reconstructed source signal and the true source signal was 92.8 % and that the separation accuracy in ERP amplitude was 91.6 %. The results show that our unsupervised separation approach can accurately separate ERP signals from nonlinear mixture source components. The outcomes provide a promising way to isolate brain responses to multiple stimulus sources during multisensory perception.
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Affiliation(s)
- Kai Zhang
- Department of Mechanical Engineering, Pennsylvania State University, University Park, USA
| | - Xiaogang Hu
- Department of Mechanical Engineering, Pennsylvania State University, University Park, USA; Department of Kinesiology, Pennsylvania State University, University Park, USA; Department of Physical Medicine & Rehabilitation, Pennsylvania State Hershey College of Medicine, USA; Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA; Center for Neural Engineering, Pennsylvania State University, University Park, USA.
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Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3987494. [PMID: 35368960 PMCID: PMC8970805 DOI: 10.1155/2022/3987494] [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: 01/12/2022] [Revised: 02/09/2022] [Accepted: 03/02/2022] [Indexed: 11/17/2022]
Abstract
Brain Computer Interface (BCI) technology commonly used to enable communication for the person with movement disability. It allows the person to communicate and control assistive robots by the use of electroencephalogram (EEG) or other brain signals. Though several approaches have been available in the literature for learning EEG signal feature, the deep learning (DL) models need to further explore for generating novel representation of EEG features and accomplish enhanced outcomes for MI classification. With this motivation, this study designs an arithmetic optimization with RetinaNet based deep learning model for MI classification (AORNDL-MIC) technique on BCIs. The proposed AORNDL-MIC technique initially exploits Multiscale Principal Component Analysis (MSPCA) approach for the EEG signal denoising and Continuous Wavelet Transform (CWT) is exploited for the transformation of 1D-EEG signal into 2D time-frequency amplitude representation, which enables to utilize the DL model via transfer learning approach. In addition, the DL based RetinaNet is applied for extracting of feature vectors from the EEG signal which are then classified with the help of ID3 classifier. In order to optimize the classification efficiency of the AORNDL-MIC technique, arithmetical optimization algorithm (AOA) is employed for hyperparameter tuning of the RetinaNet. The experimental analysis of the AORNDL-MIC algorithm on the benchmark data sets reported its promising performance over the recent state of art methodologies.
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Shamsi F, Haddad A, Najafizadeh L. Early classification of motor tasks using dynamic functional connectivity graphs from EEG. J Neural Eng 2020; 18. [PMID: 33246319 DOI: 10.1088/1741-2552/abce70] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/27/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem. APPROACH The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification. MAIN RESULTS Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data. SIGNIFICANCE Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.
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Affiliation(s)
- Foroogh Shamsi
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, NJ 08854, UNITED STATES
| | - Ali Haddad
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, NJ 08854, UNITED STATES
| | - Laleh Najafizadeh
- Electrical and Computer Engineering, Rutgers University, 94 Brett Rd, New Brunswick, New Jersey, 08901-8554, UNITED STATES
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Luo J, Gao X, Zhu X, Wang B, Lu N, Wang J. Motor imagery EEG classification based on ensemble support vector learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105464. [PMID: 32283387 DOI: 10.1016/j.cmpb.2020.105464] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 02/27/2020] [Accepted: 03/18/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Brain-computer interfaces build a communication pathway from the human brain to a computer. Motor imagery-based electroencephalogram (EEG) classification is a widely applied paradigm in brain-computer interfaces. The common spatial pattern, based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon, is one of the most popular algorithms for motor imagery-based EEG classification. Moreover, the spatiotemporal discrepancy feature based on the event-related potential phenomenon has been demonstrated to provide complementary information to ERD/ERS-based features. In this paper, aiming to improve the performance of motor imagery-based EEG classification in a few-channel situation, an ensemble support vector learning (ESVL)-based approach is proposed to combine the advantages of the ERD/ERS-based features and the event-related potential-based features in motor imagery-based EEG classification. METHODS ESVL is an ensemble learning algorithm based on support vector machine classifier. Specifically, the decision boundary with the largest interclass margin is obtained using the support vector machine algorithm, and the distances between sample points and the decision boundary are mapped to posterior probabilities. The probabilities obtained from different support vector machine classifiers are combined to make prediction. Thus, ESVL leverages the advantages of multiple trained support vector machine classifiers and makes a better prediction based on the posterior probabilities. The class discrepancy-guided sub-band-based common spatial pattern and the spatiotemporal discrepancy feature are applied to extract discriminative features, and then, the extracted features are used to train the ESVL classifier and make predictions. RESULTS The BCI Competition IV datasets 2a and 2b are employed to evaluate the performance of the proposed ESVL algorithm. Experimental comparisons with the state-of-the-art methods are performed, and the proposed ESVL-based approach achieves an average max kappa value of 0.60 and 0.71 on BCI Competition IV datasets 2a and 2b respectively. The results show that the proposed ESVL-based approach improves the performance of motor imagery-based brain-computer interfaces. CONCLUSION The proposed ESVL classifier could use the posterior probabilities to realize ensemble learning and the ESVL-based motor imagery classification approach takes advantage of the merits of ERD/ERS based feature and event-related potential based feature to improve the experimental performance.
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Affiliation(s)
- Jing Luo
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an Shaanxi, China.
| | - Xing Gao
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an Shaanxi, China
| | - Xiaobei Zhu
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an Shaanxi, China
| | - Bin Wang
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an Shaanxi, China
| | - Na Lu
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jie Wang
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
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Su J, Yang Z, Yan W, Sun W. Electroencephalogram classification in motor-imagery brain-computer interface applications based on double-constraint nonnegative matrix factorization. Physiol Meas 2020; 41:075007. [PMID: 32590360 DOI: 10.1088/1361-6579/aba07b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) are aimed at providing a new way of communication between the human brain and external devices. One of the major tasks associated with the BCI system is to improve classification performance of the motor imagery (MI) signal. Electroencephalogram (EEG) signals are widely used for the MI BCI system. The raw EEG signals are usually non-stationary time series with weak class properties, degrading the classification performance. APPROACH Nonnegative matrix factorization (NMF) has been successfully applied to pattern extraction which provides meaningful data presentation. However, NMF is unsupervised and cannot make use of the label information. Based on the label information of MI EEG data, we propose a novel method, called double-constrained nonnegative matrix factorization (DCNMF), to improve the classification performance of NMF on MI BCI. The proposed method constructs a couple of label matrices as the constraints on the NMF procedure to make the EEGs with the same class labels have the similar representation in the low-dimensional space, while the EEGs with different class labels have dissimilar representations as much as possible. Accordingly, the extracted features obtain obvious class properties, which are optimal to the classification of MI EEG. MAIN RESULTS This study is conducted on the BCI competition III datasets (I and IVa). The proposed method helps to achieve a higher average accuracy across two datasets (79.00% for dataset I, 77.78% for dataset IVa); its performance is about 10% better than the existing studies in the literature. SIGNIFICANCE Our study provides a novel solution for MI BCI analysis from the perspective of label constraint; it provides convenience for semi-supervised learning of features and significantly improves the classification performance.
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Affiliation(s)
- Jing Su
- School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou 510006, People's Republic of China
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Hu G, Zhou T, Luo S, Mahini R, Xu J, Chang Y, Cong F. Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis. Biomed Eng Online 2020; 19:61. [PMID: 32736630 PMCID: PMC7393858 DOI: 10.1186/s12938-020-00796-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 06/09/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms. RESULTS In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted. CONCLUSION Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.
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Affiliation(s)
- Guoqiang Hu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Tianyi Zhou
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.,Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, China
| | - Siwen Luo
- Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Reza Mahini
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China.
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China.
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China. .,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China. .,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, Liaoning, China. .,Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
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De La Torre Cruz J, Cañadas Quesada FJ, Ruiz Reyes N, Vera Candeas P, Carabias Orti JJ. Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-Factorization. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2679. [PMID: 32397155 PMCID: PMC7249056 DOI: 10.3390/s20092679] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 05/01/2020] [Accepted: 05/05/2020] [Indexed: 11/16/2022]
Abstract
Wheezing reveals important cues that can be useful in alerting about respiratory disorders, such as Chronic Obstructive Pulmonary Disease. Early detection of wheezing through auscultation will allow the physician to be aware of the existence of the respiratory disorder in its early stage, thus minimizing the damage the disorder can cause to the subject, especially in low-income and middle-income countries. The proposed method presents an extended version of Non-negative Matrix Partial Co-Factorization (NMPCF) that eliminates most of the acoustic interference caused by normal respiratory sounds while preserving the wheezing content needed by the physician to make a reliable diagnosis of the subject's airway status. This extension, called Informed Inter-Segment NMPCF (IIS-NMPCF), attempts to overcome the drawback of the conventional NMPCF that treats all segments of the spectrogram equally, adding greater importance for signal reconstruction of repetitive sound events to those segments where wheezing sounds have not been detected. Specifically, IIS-NMPCF is based on a bases sharing process in which inter-segment information, informed by a wheezing detection system, is incorporated into the factorization to reconstruct a more accurate modelling of normal respiratory sounds. Results demonstrate the significant improvement obtained in the wheezing sound quality by IIS-NMPCF compared to the conventional NMPCF for all the Signal-to-Noise Ratio (SNR) scenarios evaluated, specifically, an SDR, SIR and SAR improvement equals 5.8 dB, 4.9 dB and 7.5 dB evaluating a noisy scenario with SNR = -5 dB.
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Affiliation(s)
- Juan De La Torre Cruz
- Departament of Telecommunication Engineering, University of Jaen, Campus Cientifico-Tecnologico de Linares, Avda. de la Universidad, s/n, 23700 Linares, Jaen, Spain; (F.J.C.Q.); (N.R.R.); (P.V.C.); (J.J.C.O.)
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Gurve D, Delisle-Rodriguez D, Romero-Laiseca M, Cardoso V, Loterio F, Bastos T, Krishnan S. Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition. J Neural Eng 2020; 17:026029. [PMID: 31614343 DOI: 10.1088/1741-2552/ab4dba] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE This study aims to propose and validate a subject-specific approach to recognize two different cognitive neural states (relax and pedaling motor imagery (MI)) by selecting the relevant electroencephalogram (EEG) channels. The main aims of the proposed work are: (i) to reduce the computational complexity of the BCI systems during MI detection by selecting the relevant EEG channels, (ii) to reduce the amount of data overfitting that may arise due to unnecessary channels and redundant features, and (iii) to reduce the classification time for real-time BCI applications. APPROACH The proposed method selects subject-specific EEG channels and features based on their MI. In this work, we make use of non-negative matrix factorization to extract the weight of the EEG channels based on their contribution to MI detection. Further, the neighborhood component analysis is used for subject-specific feature selection. MAIN RESULTS We executed the experiments using EEG signals recorded for MI where ten healthy subjects performed MI movement of the lower limb to generate motor commands. An average accuracy of 96.66%, average true positive rate (TPR) of 97.77%, average false positives rate of 4.44%, and average Kappa of 93.33% were obtained. The proposed subject-specific EEG channel selection based MI recognition system provides 13.20% improvement in detection accuracy, and 27% improvement in Kappa value with less number of EEG channels compared to the results obtained using all EEG channels. SIGNIFICANCE The proposed subject-specific BCI system has been found significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduce computational complexity and processing time (two times faster) but also improve the MI detection performance. The proposed method selects EEG locations related to the foot movement, which may be relevant for neuro-rehabilitation using lower-limb movements that may provide a real-time and more natural interface between patient and robotic device.
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Affiliation(s)
- Dharmendra Gurve
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada. Author to whom any correspondence should be addressed
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Han W, Xie S, Yang Z, Zhou S, Huang H. Heart sound classification using the SNMFNet classifier. Physiol Meas 2019; 40:105003. [PMID: 31533092 DOI: 10.1088/1361-6579/ab45c8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Heart sound classification still suffers from the challenges involved in achieving high accuracy in the case of small samples. Dimension reduction attempts to extract low-dimensional features with more discriminability from high-dimensional spaces or raw data, and is popular in learning predictive models that target small sample problems. However, it can also be harmful to classification, because any reduction has the potential to lose information containing category attributes. APPROACH For this, a novel SNMFNet classifier is designed to directly associate the dimension reduction process with the classification procedure used for promoting feature dimension reduction to follow the approach that is beneficial for classification, thus making the low-dimensional features more distinguishable and addressing the challenge facing heart sound classification in small samples. MAIN RESULTS We evaluated our method and representative methods using a public heart sound dataset. The experimental results demonstrate that our method outperforms all comparative models with an obvious improvement in small samples. Furthermore, even if used with relatively sufficient samples, our method performs at least as well as the baseline that uses the same high-dimensional features. SIGNIFICANCE The proposed SNMFNet classifier significantly to improves the small sample problem in heart sound classification.
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Affiliation(s)
- Wei Han
- School of Automation, Guangdong University of Technology, Guangzhou, People's Republic of China. Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, People's Republic of China
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Luo J, Feng Z, Lu N. Spatio-temporal discrepancy feature for classification of motor imageries. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.07.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2017; 2017:2727856. [PMID: 28874909 PMCID: PMC5569879 DOI: 10.1155/2017/2727856] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/14/2017] [Accepted: 07/02/2017] [Indexed: 11/17/2022]
Abstract
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.
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Lu N, Li T, Ren X, Miao H. A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines. IEEE Trans Neural Syst Rehabil Eng 2017; 25:566-576. [DOI: 10.1109/tnsre.2016.2601240] [Citation(s) in RCA: 245] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Multichannel interictal spike activity detection using time–frequency entropy measure. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:413-425. [DOI: 10.1007/s13246-017-0550-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 04/05/2017] [Indexed: 11/26/2022]
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Luo J, Feng Z, Zhang J, Lu N. Dynamic frequency feature selection based approach for classification of motor imageries. Comput Biol Med 2016; 75:45-53. [DOI: 10.1016/j.compbiomed.2016.03.004] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Revised: 03/08/2016] [Accepted: 03/12/2016] [Indexed: 10/22/2022]
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