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Pan L, Wang K, Xu L, Sun X, Yi W, Xu M, Ming D. Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals. J Neural Eng 2023; 20:066011. [PMID: 37931299 DOI: 10.1088/1741-2552/ad0a01] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/06/2023] [Indexed: 11/08/2023]
Abstract
Objective.Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.Approach.We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).Main results.Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples.Significance.These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.
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Affiliation(s)
- Lincong Pan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Kun Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, People's Republic of China
| | - Lichao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xinwei Sun
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Weibo Yi
- Beijing Machine and Equipment Institute, Beijing 100192, People's Republic of China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, People's Republic of China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin 300392, People's Republic of China
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Hu H, Pu Z, Li H, Liu Z, Wang P. Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:8526. [PMID: 36366225 PMCID: PMC9658317 DOI: 10.3390/s22218526] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.
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Affiliation(s)
| | | | | | | | - Peng Wang
- Correspondence: ; Tel.: +86-10-6277-2007
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Antony MJ, Sankaralingam BP, Mahendran RK, Gardezi AA, Shafiq M, Choi JG, Hamam H. Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197596. [PMID: 36236694 PMCID: PMC9573537 DOI: 10.3390/s22197596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 05/30/2023]
Abstract
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.
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Affiliation(s)
- Mary Judith Antony
- Department of Computer Science and Engineering, Loyola-ICAM College of Engineering and Technology, Chennai 600034, India
| | | | - Rakesh Kumar Mahendran
- Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600062, India
| | - Akber Abid Gardezi
- Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
| | - Muhammad Shafiq
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Jin-Ghoo Choi
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Habib Hamam
- Faculty of Engineering, Uni de Moncton, Moncton, NB E1A 3E9, Canada
- International Institute of Technology and Management, Commune d’Akanda, BP, Libreville 1989, Gabon
- School of Electrical and Electronic Engineering Science, Department of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
- Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
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Wen Y, He W, Zhang Y. A new attention-based 3D densely connected cross-stage-partial network for motor imagery classification in BCI. J Neural Eng 2022; 19. [PMID: 36130589 DOI: 10.1088/1741-2552/ac93b4] [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: 04/19/2022] [Accepted: 09/21/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals. APPROACH This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network. MAIN RESULTS The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability. SIGNIFICANCE The experimental results show that our method is promising for improving the performance of MI-BCI. As a general framework based on time-series classification, it can be applied to BCI-related fields.
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Affiliation(s)
- Yintang Wen
- Yanshan University, Qinhuangdao, Qinhuangdao, Hebei, 066004, CHINA
| | - Wenjing He
- Yanshan University, Qinhuangdao, Qinhuangdao, Hebei, 066004, CHINA
| | - Yuyan Zhang
- Yanshan University, Qinhuangdao, Qinhuangdao, Hebei, 066004, CHINA
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Gao Y, Lin T, Pan J, Nie F, Xie Y. Fuzzy Sparse Deviation Regularized Robust Principal Component Analysis. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 31:5645-5660. [PMID: 35994528 DOI: 10.1109/tip.2022.3199086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Robust principal component analysis (RPCA) is a technique that aims to make principal component analysis (PCA) robust to noise samples. The current modeling approaches of RPCA were proposed by analyzing the prior distribution of the reconstruction error terms. However, these methods ignore the influence of samples with large reconstruction errors, as well as the valid information of these samples in principal component space, which will degrade the ability of PCA to extract the principal component of data. In order to solve this problem, Fuzzy sparse deviation regularized robust principal component Analysis (FSD-PCA) is proposed in this paper. First, FSD-PCA learns the principal components by minimizing the square of l2 -norm-based reconstruction error. Then, FSD-PCA introduces sparse deviation on reconstruction error term to relax the samples with large bias, thus FSD-PCA can process noise and principal components of samples separately as well as improve the ability of FSD-PCA for retaining the principal component information. Finally, FSD-PCA estimates the prior probability of each sample by fuzzy weighting based on the relaxed reconstruction error, which can improve the robustness of the model. The experimental results indicate that the proposed model performs excellent robustness against different types of noise than the state-of-art algorithms, and the sparse deviation term enables FSD-PCA to process noise information and principal component information separately, so FSD-PCA can filter the noise information of an image and restore the corrupted image.
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6
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Zhang S, Zhu Z, Zhang B, Feng B, Yu T, Li Z, Zhang Z, Huang G, Liang Z. Overall optimization of CSP based on ensemble learning for motor imagery EEG decoding. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zheng L, Feng W, Ma Y, Lian P, Xiao Y, Yi Z, Wu X. Ensemble learning method based on temporal, spatial features with multi-scale filter banks for motor imagery EEG classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103634] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhao X, Jin J, Xu R, Li S, Sun H, Wang X, Cichocki A. A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller. Front Hum Neurosci 2022; 16:875851. [PMID: 35754766 PMCID: PMC9231363 DOI: 10.3389/fnhum.2022.875851] [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: 02/14/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
The P300-based brain-computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.
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Affiliation(s)
- Xueqing Zhao
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
- Shenzhen Research Institute of East China University of Technology, Shenzhen, China
| | - Ren Xu
- g.tec medical engineering GmbH, Graz, Austria
| | - Shurui Li
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Hao Sun
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology, Moscow, Russia
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
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Luo J, Mao Q, Wang Y, Shi Z, Hei X. Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey. BRAIN SCIENCE ADVANCES 2022. [DOI: 10.26599/bsa.2022.9050011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Objective: From September 10 to 13, 2021, the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing, China. Eleven teams participated in the Algorithm Contest of Calibration-free Motor Imagery BCI. The participants employed both traditional electroencephalograph (EEG) analysis methods and deep learning-based methods in the contest. In this paper, we reviewed the algorithms utilized by the participants, extracted the trends and highlighted interesting approaches from these methods to inform future contests and research recommendations. Method: First, we analyzed the algorithms in separate steps, including EEG channel and signal segment setup, prepossessing technology, and classification model. Then, we emphasized the highlights of each algorithm. Finally, we compared the competition algorithm with the SOTA algorithm. Results: The algorithm employed in the finals performed better than that of the SOTA algorithm. During the final stage of the contest, four of the top five teams used convolutional neural network models, suggesting that with the rapid development of deep learning, convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.
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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 710054, Shaanxi, China
| | - Qi Mao
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, Shaanxi, China
| | - Yaojie Wang
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, Shaanxi, China
| | - Zhenghao Shi
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, Shaanxi, China
| | - Xinhong Hei
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710054, Shaanxi, China
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EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2021:6668859. [PMID: 35530739 PMCID: PMC9071993 DOI: 10.1155/2021/6668859] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/01/2021] [Accepted: 03/17/2021] [Indexed: 11/18/2022]
Abstract
In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.
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Machine-learning-enabled adaptive signal decomposition for a brain-computer interface using EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103526] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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Rithwik P, Benzy V, Vinod A. High accuracy decoding of motor imagery directions from EEG-based brain computer interface using filter bank spatially regularised common spatial pattern method. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103241] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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13
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Zabcikova M, Koudelkova Z, Jasek R, Navarro JJL. Recent Advances and Current Trends in Brain-Computer Interface (BCI) Research and Their Applications. Int J Dev Neurosci 2021; 82:107-123. [PMID: 34939217 DOI: 10.1002/jdn.10166] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/16/2021] [Accepted: 12/18/2021] [Indexed: 11/06/2022] Open
Abstract
Brain-Computer Interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, one hundred most cited articles from the WOS database were selected over the last four years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.
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Affiliation(s)
- Martina Zabcikova
- Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
| | - Zuzana Koudelkova
- Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
| | - Roman Jasek
- Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
| | - José Javier Lorenzo Navarro
- Departamento de Informática y Sistemas, Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
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Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1462369. [PMID: 34858491 PMCID: PMC8632405 DOI: 10.1155/2021/1462369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/24/2021] [Accepted: 11/02/2021] [Indexed: 11/17/2022]
Abstract
Objective Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors' methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.
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Pei Y, Luo Z, Zhao H, Xu D, Li W, Yan Y, Yan H, Xie L, Xu M, Yin E. A Tensor-Based Frequency Features Combination Method for Brain-Computer Interfaces. IEEE Trans Neural Syst Rehabil Eng 2021; 30:465-475. [PMID: 34735347 DOI: 10.1109/tnsre.2021.3125386] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
With the development of the brain-computer interface (BCI) community, motor imagery-based BCI system using electroencephalogram (EEG) has attracted increasing attention because of its portability and low cost. Concerning the multi-channel EEG, the frequency component is one of the most critical features. However, insufficient extraction hinders the development and application of MI-BCIs. To deeply mine the frequency information, we proposed a method called tensor-based frequency feature combination (TFFC). It combined tensor-to-vector projection (TVP), fast fourier transform (FFT), common spatial pattern (CSP) and feature fusion to construct a new feature set. With two datasets, we used different classifiers to compare TFFC with the state-of-the-art feature extraction methods. The experimental results showed that our proposed TFFC could robustly improve the classification accuracy of about 5% (p < 0.01). Moreover, visualization analysis implied that the TFFC was a generalization of CSP and Filter Bank CSP (FBCSP). Also, a complementarity between weighted narrowband features (wNBFs) and broadband features (BBFs) was observed from the averaged fusion ratio. This article certificates the importance of frequency information in the MI-BCI system and provides a new direction for designing a feature set of MI-EEG.
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Jin J, Xiao R, Daly I, Miao Y, Wang X, Cichocki A. Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:4814-4825. [PMID: 32833646 DOI: 10.1109/tnnls.2020.3015505] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.
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Li B, Zhang Z, Duan F, Yang Z, Zhao Q, Sun Z, Solé-Casals J. Component-mixing strategy: A decomposition-based data augmentation algorithm for motor imagery signals. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Sun H, Jin J, Xu R, Cichocki A. Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain-Computer Interfaces. Int J Neural Syst 2021; 31:2150040. [PMID: 34376122 DOI: 10.1142/s0129065721500404] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Motor imagery (MI) based brain-computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corresponding features obtained from the traditional CSP algorithm. In this paper, we designed a novel CSP feature selection framework that combines the filter method and the wrapper method. We first evaluated the importance of every CSP feature by the infinite latent feature selection method. Meanwhile, we calculated Wasserstein distance between feature distributions of the same feature under different tasks. Then, we redefined the importance of every CSP feature based on two indicators mentioned above, which eliminates half of CSP features to create a new CSP feature subspace according to the new importance indicator. At last, we designed the improved binary gravitational search algorithm (IBGSA) by rebuilding its transfer function and applied IBGSA on the new CSP feature subspace to find the optimal feature set. To validate the proposed method, we conducted experiments on three public BCI datasets and performed a numerical analysis of the proposed algorithm for MI classification. The accuracies were comparable to those reported in related studies and the presented model outperformed other methods in literature on the same underlying data.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai, P. R. China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai, P. R. China
| | - Ren Xu
- Guger Technologies OG, Graz, Austria
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (SKOLTECH), 121205 Moscow, Russia.,Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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19
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Amodeo M, Arpaia P, Buzio M, Di Capua V, Donnarumma F. Hysteresis Modeling in Iron-Dominated Magnets Based on a Multi-Layered Narx Neural Network Approach. Int J Neural Syst 2021; 31:2150033. [PMID: 34296651 DOI: 10.1142/s0129065721500337] [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] [Indexed: 11/18/2022]
Abstract
A full-fledged neural network modeling, based on a Multi-layered Nonlinear Autoregressive Exogenous Neural Network (NARX) architecture, is proposed for quasi-static and dynamic hysteresis loops, one of the most challenging topics for computational magnetism. This modeling approach overcomes drawbacks in attaining better than percent-level accuracy of classical and recent approaches for accelerator magnets, that combine hybridization of standard hysteretic models and neural network architectures. By means of an incremental procedure, different Deep Neural Network Architectures are selected, fine-tuned and tested in order to predict magnetic hysteresis in the context of electromagnets. Tests and results show that the proposed NARX architecture best fits the measured magnetic field behavior of a reference quadrupole at CERN. In particular, the proposed modeling framework leads to a percent error below 0.02% for the magnetic field prediction, thus outperforming state of the art approaches and paving a very promising way for future real time applications.
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Affiliation(s)
- Maria Amodeo
- Department of Electronics and Telecommunications (DET), Polytechnic University of Turin, Turin 10129, Italy.,Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Pasquale Arpaia
- Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Marco Buzio
- Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Vincenzo Di Capua
- Instrumentation and Measurement Laboratory for Particle Accelerator Laboratory (IMPALab), Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples 80100, Italy.,Technology Department, CERN - European Organization for Nuclear Research, 1211 Meyrin, Switzerland
| | - Francesco Donnarumma
- Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Via San Martino della Battaglia, 44, Rome 00185, Italy
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20
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Decoding the torque of lower limb joints from EEG recordings of pre-gait movements using a machine learning scheme. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.03.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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21
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Mirzaei S, Ghasemi P. EEG motor imagery classification using dynamic connectivity patterns and convolutional autoencoder. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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22
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Kumar S, Tsunoda T, Sharma A. SPECTRA: a tool for enhanced brain wave signal recognition. BMC Bioinformatics 2021; 22:195. [PMID: 34078274 PMCID: PMC8170968 DOI: 10.1186/s12859-021-04091-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 03/21/2021] [Indexed: 12/31/2022] Open
Abstract
Background Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain–computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP). Results The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively.
Conclusions Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
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Affiliation(s)
- Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.,Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.,Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia
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23
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Ra JS, Li T, Li Y. A novel spectral entropy-based index for assessing the depth of anaesthesia. Brain Inform 2021; 8:10. [PMID: 33978842 PMCID: PMC8116386 DOI: 10.1186/s40708-021-00130-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/12/2021] [Indexed: 12/05/2022] Open
Abstract
Anaesthesia is a state of temporary controlled loss of awareness induced for medical operations. An accurate assessment of the depth of anaesthesia (DoA) helps anesthesiologists to avoid awareness during surgery and keep the recovery period short. However, the existing DoA algorithms have limitations, such as not robust enough for different patients and having time delay in assessment. In this study, to develop a reliable DoA measurement method, pre-denoised electroencephalograph (EEG) signals are divided into ten frequency bands (α, β1, β2, β3, β4, β, βγ, γ, δ and θ), and the features are extracted from different frequency bands using spectral entropy (SE) methods. SE from the beta-gamma frequency band (21.5–38.5 Hz) and SE from the beta frequency band show the highest correlation (R-squared value: 0.8458 and 0.7312, respectively) with the most popular DoA index, bispectral index (BIS). In this research, a new DoA index is developed based on these two SE features for monitoring the DoA. The highest Pearson correlation coefficient by comparing the BIS index for testing data is 0.918, and the average is 0.80. In addition, the proposed index shows an earlier reaction than the BIS index when the patient goes from deep anaesthesia to moderate anaesthesia, which means it is more suitable for the real-time DoA assessment. In the case of poor signal quality (SQ), while the BIS index exhibits inflexibility with cases of poor SQ, the new proposed index shows reliable assessment results that reflect the clinical observations.
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Affiliation(s)
- Jee Sook Ra
- School of Sciences, University of Southern Queensland, West St, Darling Heights, Toowomba, QLD, 4350, Australia.
| | - Tianning Li
- School of Sciences, University of Southern Queensland, West St, Darling Heights, Toowomba, QLD, 4350, Australia
| | - Yan Li
- School of Sciences, University of Southern Queensland, West St, Darling Heights, Toowomba, QLD, 4350, Australia
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24
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Singh Malan N, Sharma S. Time window and frequency band optimization using regularized neighbourhood component analysis for Multi-View Motor Imagery EEG classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102550] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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25
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Dagdevir E, Tokmakci M. Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102548] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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26
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Miao Y, Jin J, Daly I, Zuo C, Wang X, Cichocki A, Jung TP. Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification. IEEE Trans Neural Syst Rehabil Eng 2021; 29:699-707. [PMID: 33819158 DOI: 10.1109/tnsre.2021.3071140] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.
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28
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Fu R, Han M, Bao T, Wang F, Shi P. Discrimination Improvement Through Undesirable Feedback in Coupling Object Manipulation Tasks. Int J Neural Syst 2021; 31:2150012. [PMID: 33573533 DOI: 10.1142/s012906572150012x] [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] [Indexed: 11/18/2022]
Abstract
Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is [Formula: see text]. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts.
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Affiliation(s)
- Rongrong Fu
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, P. R. China
| | - Mengmeng Han
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, P. R. China
| | - Tiantian Bao
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, P. R. China
| | - Fuwang Wang
- School of Mechanical Engineering, Northeastern Electric Power University, P. R. China
| | - Peiming Shi
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao, P. R. China
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29
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Luo J, Shi W, Lu N, Wang J, Chen H, Wang Y, Lu X, Wang X, Hei X. Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs. J Neural Eng 2021; 18. [PMID: 33540387 DOI: 10.1088/1741-2552/abe357] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/04/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Motor imagery (MI) EEG signals vary greatly among subjects, so scholarly research on motor imagery-based brain-computer interfaces (BCIs) has mainly focused on single-subject systems or subject-dependent systems. However, the single-subject model is applicable only to the target subject, and the small sample number greatly limits the performance of the model. This paper aims to study a convolutional neural network to achieve an adaptable MI-BCI that is applicable to multiple subjects. APPROACH In this paper, a twin cascaded softmax convolutional neural network (TCSCNN) is proposed for multisubject MI-BCIs. The proposed TCSCNN is independent and can be applied to any single-subject MI classification CNN model. First, to reduce the influence of individual differences, subject recognition and MI recognition are accomplished simultaneously. A cascaded softmax structure consisting of two softmax layers, related to subject recognition and MI recognition, is subsequently applied. Second, to improve the MI classification precision, a twin network structure is proposed on the basis of ensemble learning. TCSCNN is built by combining a cascaded softmax structure and twin network structure. MAIN RESULTS Experiments were conducted on three popular CNN models (EEGNet and Shallow ConvNet and Deep ConvNet from EEGDecoding) and three public datasets (BCI Competition IV datasets 2a and 2b and the High-Gamma dataset) to verify the performance of the proposed TCSCNN. The results show that compared with the state-of-the-art CNN model, the proposed TCSCNN obviously improves the precision and convergence of multisubject MI recognition. SIGNIFICANCE This study provides a promising scheme for multisubject MI-BCI, reflecting the progress made in the development and application of MI-BCIs.
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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, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
| | - Weiwei Shi
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering , Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
| | - Na Lu
- Systems Engineering Institute, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, Xi'an, 710049, CHINA
| | - Jie Wang
- State Key Laboratory for Manufacturing System Engineering, System Engineering Institute, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, Xi'an, Shaanxi, 710049, CHINA
| | - Hao Chen
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
| | - Yaojie Wang
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering , Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
| | - Xiaofeng Lu
- School of computer science, Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
| | - Xiaofan Wang
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering , Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
| | - Xinhong Hei
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering , Xi'an University of Technology, No. 5, Jinhua South Road, Xi'an, Shaanxi Province, Xi'an, Shaanxi, 710048, CHINA
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Kumar S, Sharma R, Sharma A. OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals. PeerJ Comput Sci 2021; 7:e375. [PMID: 33817023 PMCID: PMC7959638 DOI: 10.7717/peerj-cs.375] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
A human-computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In this work, we address the problem of selecting the appropriate filtering frequency band while also achieving a good system performance by proposing a frequency-based approach using long short-term memory network (LSTM) for recognizing different brain wave signals. Adaptive filtering using genetic algorithm is incorporated for a hybrid system utilizing common spatial pattern and LSTM network. The proposed method (OPTICAL+) achieved an overall average classification error rate of 30.41% and a kappa coefficient value of 0.398, outperforming the state-of-the-art methods. The proposed OPTICAL+ predictor can be used to develop improved HCI systems that will aid in neurorehabilitation and may also be beneficial for sleep stage classification and seizure detection.
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Affiliation(s)
- Shiu Kumar
- School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji
| | - Ronesh Sharma
- School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji
| | - Alok Sharma
- STEMP, University of the South Pacific, Suva, Fiji
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
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31
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Zuo C, Jin J, Xu R, Wu L, Liu C, Miao Y, Wang X. Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain-computer interfaces. J Neural Eng 2021; 18. [PMID: 33524961 DOI: 10.1088/1741-2552/abe20f] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 02/01/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs. APPROACH In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets with different distributions by clustering decomposition. Then a set of heterogeneous classifiers was trained on each sub-data set for generating a diversified classifier search space. To obtain the optimal classifier combination, the ensemble learning was formulated as a multi-objective optimization problem and a stochastic fractal based binary multi-objective fruit fly optimization algorithm was proposed for solving the ensemble learning problem. MAIN RESULTS The proposed method was validated on two public EEG datasets (BCI Competition IV datasets IIb and BCI Competition IV dataset IIa) and compared with several other competing classification methods. Experimental results showed that the proposed CDECL based methods can effectively construct a diversity ensemble classifier and exhibits superior classification performance in comparison with several competing methods. SIGNIFICANCE The proposed method is promising for improving the performance of MI-based BCIs.
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Affiliation(s)
- Cili Zuo
- East China University of Science and Technology, 130 Meilong road, Shanghai, Shanghai, 200237, CHINA
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, SHANGHAI, 200237, CHINA
| | - Ren Xu
- Guger Technologies OG, Sierningstrasse 14, Graz, 8020, AUSTRIA
| | - Lianghong Wu
- Hunan University of Science and Technology, Tiaoyuan Road, Xiangtan, 411201, CHINA
| | - Chang Liu
- East China University of Science and Technology, 130 Meilong Road, Shanghai, Shanghai, 200237, CHINA
| | - Yangyang Miao
- East China University of Science and Technology, 130 Meilong raod, Shanghai, Shanghai, 200237, CHINA
| | - Xingyu Wang
- East China University of Science and Technology, 130 Meilong Road, Shanghai, Shanghai, 200237, CHINA
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32
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Li C, Zhou W, Liu G, Zhang Y, Geng M, Liu Z, Wang S, Shang W. Seizure Onset Detection Using Empirical Mode Decomposition and Common Spatial Pattern. IEEE Trans Neural Syst Rehabil Eng 2021; 29:458-467. [PMID: 33507872 DOI: 10.1109/tnsre.2021.3055276] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Automatic seizure onset detection plays an important role in epilepsy diagnosis. In this paper, a novel seizure onset detection method is proposed by combining empirical mode decomposition (EMD) of long-term scalp electroencephalogram (EEG) with common spatial pattern (CSP). First, wavelet transform (WT) and EMD are employed on EEG recordings respectively for filtering pre-processing and time-frequency decomposition. Then CSP is applied to reduce the dimension of multi-channel time-frequency representation, and the variance is extracted as the only feature. Afterwards, a support vector machine (SVM) group consisting of ten SVMs is served as a robust classifier. Finally, the post-processing is adopted to acquire a higher recognition rate and reduce the false detection rate. The results obtained from CHB-MIT database of 977 h scalp EEG recordings reveal that the proposed system can achieve a segment-based sensitivity of 97.34% with a specificity of 97.50% and an event-based sensitivity of 98.47% with a false detection rate of 0.63/h. This proposed detection system was also validated on a clinical scalp EEG database from the Second Hospital of Shandong University, and the system yielded a sensitivity of 93.67% and a specificity of 96.06%. At the event-based level, a sensitivity of 99.39% and a false detection rate of 0.64/h were obtained. Furthermore, this work showed that the CSP spatial filter was helpful to identify EEG channels involved in seizure onsets. These satisfactory results indicate that the proposed system may provide a reference for seizure onset detection in clinical applications.
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33
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Chen X, Tao X, Wang FL, Xie H. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05588-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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34
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Zhang J, Wang M. A survey on robots controlled by motor imagery brain-computer interfaces. COGNITIVE ROBOTICS 2021. [DOI: 10.1016/j.cogr.2021.02.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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35
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Zhu Y, Wang X, Mathiak K, Toiviainen P, Ristaniemi T, Xu J, Chang Y, Cong F. Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression. Int J Neural Syst 2020; 31:2150001. [PMID: 33353528 DOI: 10.1142/s0129065721500015] [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: 11/18/2022]
Abstract
To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.
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Affiliation(s)
- Yongjie Zhu
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China.,Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland.,Department of Computer Science, University of Helsinki, Finland
| | - Xiaoyu Wang
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, Pauwelsstraße 30, D-52074 Aachen, Germany
| | - Petri Toiviainen
- Department of Music, Art and Culture Studies, University of Jyväskylä 40014, Jyväskylä, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, P. R. China
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, P. R. China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology 116024, Dalian, P. R. China.,Faculty of Information Technology, University of Jyväskylä 40014, Jyväskylä, Finland.,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, P. R. China.,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province Dalian University of Technology, Dalian, P. R. China
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Arpaia P, Donnarumma F, Esposito A, Parvis M. Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces. Int J Neural Syst 2020; 31:2150003. [PMID: 33353529 DOI: 10.1142/s0129065721500039] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77-83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.
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Affiliation(s)
- Pasquale Arpaia
- Department of Electrical Engineering and Information Technology (DIETI), Universita' degli Studi di Napoli Federico II, Naples, Italy.,Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Italy
| | - Francesco Donnarumma
- Institute of Cognitive Sciences and Technologies, National Research Council (ISTC-CNR), Rome, Italy.,Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Italy
| | - Antonio Esposito
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Turin, Italy.,Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Italy
| | - Marco Parvis
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Turin, Italy.,Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Italy
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Xue J, Ren F, Sun X, Yin M, Wu J, Ma C, Gao Z. A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding. Neural Plast 2020; 2020:8863223. [PMID: 33505456 PMCID: PMC7787825 DOI: 10.1155/2020/8863223] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/22/2020] [Accepted: 11/04/2020] [Indexed: 12/11/2022] Open
Abstract
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.
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Affiliation(s)
- Juntao Xue
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Feiyue Ren
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Xinlin Sun
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Miaomiao Yin
- Department of Neurorehabilitation and Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China
| | - Jialing Wu
- Department of Neurorehabilitation and Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China
| | - Chao Ma
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Zhongke Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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38
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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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García-Martínez B, Fernández-Caballero A, Zunino L, Martínez-Rodrigo A. Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics. Cognit Comput 2020. [DOI: 10.1007/s12559-020-09789-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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40
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Shao X, Lin M. Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification. Cogn Neurodyn 2020; 14:689-696. [PMID: 33014181 PMCID: PMC7501359 DOI: 10.1007/s11571-020-09620-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 06/21/2020] [Accepted: 07/19/2020] [Indexed: 11/26/2022] Open
Abstract
Canonical correlation analysis (CCA) method and its extended methods have been widely and successfully applied to the frequency recognition in SSVEP-based BCI systems. As a state-of-the-art extended method, filter bank canonical correlation analysis has higher accuracy and information transmission rate (ITR) than CCA. However, in the CCA method, the temporally local structure of samples has not been well considered. In this correspondence, we proposed termed temporally local canonical correlation analysis (TCCA). In this new method, the original covariance matrix was replaced by the temporally local covariance matrix. Furthermore, we proposed an improved frequency identification method of filter bank based on TCCA, named filter bank temporally local canonical correlation analysis (FBTCCA). In the offline environment, we used a leave-one-subject-out validation strategy on datasets of ten testees to optimize the parameters of TCCA and FBTCCA and evaluate the two algorithms. The experimental results affirm that TCCA markedly outperformed CCA, and FBTCCA obtained the highest accuracy among the four methods. This study corroborates that TCCA-based approaches have great potential for implementing short time window SSVEP-based BCI systems.
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Affiliation(s)
- Xinghan Shao
- School of Mechanical Engineering, Shandong University, Jinan, 250000 China
| | - Mingxing Lin
- School of Mechanical Engineering, Shandong University, Jinan, 250000 China
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41
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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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42
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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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43
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Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. J Med Syst 2020; 44:176. [DOI: 10.1007/s10916-020-01639-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 08/05/2020] [Indexed: 11/26/2022]
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44
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Zhang S, Zhu Z, Zhang B, Feng B, Yu T, Li Z. The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification. SENSORS 2020; 20:s20174749. [PMID: 32842635 PMCID: PMC7506901 DOI: 10.3390/s20174749] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/11/2020] [Accepted: 08/18/2020] [Indexed: 11/16/2022]
Abstract
The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.
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Affiliation(s)
- Shaorong Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (S.Z.); (B.Z.); (Z.L.)
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China;
| | - Zhibin Zhu
- School of Mathematics and Computational Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China
- Correspondence:
| | - Benxin Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (S.Z.); (B.Z.); (Z.L.)
| | - Bao Feng
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China;
| | - Tianyou Yu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510000, China;
| | - Zhi Li
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; (S.Z.); (B.Z.); (Z.L.)
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China;
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45
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Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces. J Neurosci Methods 2020; 340:108725. [DOI: 10.1016/j.jneumeth.2020.108725] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/02/2020] [Accepted: 04/03/2020] [Indexed: 11/20/2022]
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46
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Jiang J, Wang C, Wu J, Qin W, Xu M, Yin E. Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs. Front Hum Neurosci 2020; 14:231. [PMID: 32714167 PMCID: PMC7344307 DOI: 10.3389/fnhum.2020.00231] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 05/25/2020] [Indexed: 11/19/2022] Open
Abstract
Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.
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Affiliation(s)
- Jing Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Chunhui Wang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Jinghan Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Wei Qin
- Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Erwei Yin
- Unmanned Systems Research Center, National Innovation Institute of Defense Technology, Academy of Military Sciences China, Beijing, China.,Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China
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Fu R, Han M, Tian Y, Shi P. Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis. J Neurosci Methods 2020; 343:108833. [PMID: 32619588 DOI: 10.1016/j.jneumeth.2020.108833] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/24/2020] [Accepted: 06/25/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND The classification of psychological tasks such as motor imagery based on electroencephalography (EEG) signals is an essential issue in the brain computer interface (BCI) system. The feature extraction is an important issue for improving classification accuracy of BCI system. NEW METHOD For extracting discriminative features, common spatial pattern (CSP) is an effective feature extraction method. However, features extracted by CSP are dense, and even feature patterns are repeatedly selected in the feature space. A sparse CSP algorithm is proposed, which embeds the sparse techniques and iterative search into the CSP. To improve the classification performance, two regularization parameters are added to the traditional linear discriminant analysis (LDA). RESULTS The sparse CSP algorithm can select several channels of EEG signals with the most obvious features. The improved regularized discriminant analysis is used to solve the singularity problem and improve the feature classification accuracy. Comparison with Existing Method(s): The proposed algorithm was evaluated by the data set I of the IVth BCI competition and our dataset. The experimental results of the BCI competition dataset show that accuracy of the improved algorithm is 10.75 % higher than that of the traditional algorithm. Comparing with the currently existing methods for the same data, it also shows excellent classification performance. The effectiveness of the improved algorithm is also shown in experiments on our dataset. CONCLUSIONS It sufficiently proves that the improved algorithm proposed in this paper improves the classification performance of motor intent recognition.
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Affiliation(s)
- Rongrong Fu
- Yanshan University School of Electrical Engineering, 066004, China
| | - Mengmeng Han
- Yanshan University School of Electrical Engineering, 066004, China
| | - Yongsheng Tian
- Yanshan University School of Electrical Engineering, 066004, China.
| | - Peiming Shi
- Yanshan University School of Electrical Engineering, 066004, China
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48
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Huang Z, Qiu Y, Sun W. Recognition of motor imagery EEG patterns based on common feature analysis. BRAIN-COMPUTER INTERFACES 2020. [DOI: 10.1080/2326263x.2020.1783170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Zhenhao Huang
- School of Automation, Guangdong University of Technology, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Smart Manufacturing, Guangzhou, China
| | - Yichun Qiu
- School of Automation, Guangdong University of Technology, Guangzhou, China
- Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing, Ministry of Education, Guangzhou, China
| | - Weijun Sun
- School of Automation, Guangdong University of Technology, Guangzhou, China
- Guangdong Key Laboratory of IoT Information Technology, Guangzhou, China
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49
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Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn 2020; 15:141-156. [PMID: 33786085 DOI: 10.1007/s11571-020-09608-3] [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: 12/17/2019] [Revised: 05/09/2020] [Accepted: 06/13/2020] [Indexed: 11/27/2022] Open
Abstract
Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.
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50
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Shen L, Dong X, Li Y. Analysis and classification of hybrid EEG features based on the depth DRDS videos. J Neurosci Methods 2020; 338:108690. [PMID: 32194131 DOI: 10.1016/j.jneumeth.2020.108690] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 02/28/2020] [Accepted: 03/15/2020] [Indexed: 01/24/2023]
Abstract
BACKGROUND Stereo vision cognition is a crucial advanced function of human beings, and stereoscopic acuity is an important index to detect stereo vision. Electroencephalograph (EEG) is an effective method of detection. Therefore, it has great significance to research the relationship between stereoscopic acuity and EEG signals for the development of 3D technology. NEW METHOD This paper proposes a multi-channel selection sparse time window common spatial group (MCS-STWCSG) multi-classification method. Firstly, a channel selection method based on improved common spatial pattern- (CSP-) rank is applied to select optimal channels to reduce redundant signal. Secondly, based on the one vs. one (OVO) computational model, we extend traditional CSP to the common spatial group (CSG) to implement three-classification. Finally, this paper optimizes time-frequency characteristics and hybrid signal features by sparse regression and utilizes a support vector machine (SVM) with radial basis function (RBF) kernel to identify depth dynamic random dot stereogram (DRDS) video tasks. RESULTS The selected channels are all located in and near the occipital region and time-frequency characteristics can acquire better classification results compared with frequency characteristics. The highest classification result can reach 94.67%. COMPARISON WITH EXISTING METHODS The MCS-STWCSG multi-classification method optimizes features from multiple aspects and its performance is obviously better than other methods for hybrid EEG signals of depth DRDS. CONCLUSIONS Channel selection and time-frequency segmentation for feature extraction and classification algorithm of EEG signals can increase the classification accuracy. It proves the feasibility and accuracy of the proposed method.
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Affiliation(s)
- Lili Shen
- Tianjin University, Shool of Electrical and Information Engineering, Weijin Road, Tianjin 300072, China.
| | - Xinxin Dong
- Tianjin University, Shool of Electrical and Information Engineering, Weijin Road, Tianjin 300072, China
| | - Yueping Li
- Tianjin Eye Hospital, Clinical College of Ophthalmology of Tianjin Medical University, Tianjin Key Laboratory of Ophthalmology and Vision Science, Tianjin 300020, China.
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