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Venu K, Natesan P. Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task. BIOMED ENG-BIOMED TE 2024; 69:125-140. [PMID: 37935217 DOI: 10.1515/bmt-2023-0407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 09/30/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task. METHODS The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics. RESULTS A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST. CONCLUSIONS The proposed method achieved effective classification performance in terms of performance measures.
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Affiliation(s)
- K Venu
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
| | - P Natesan
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
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2
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Ma C, Shi Y, Huang Y, Dai G. Raman spectroscopy-based prediction of ofloxacin concentration in solution using a novel loss function and an improved GA-CNN model. BMC Bioinformatics 2023; 24:409. [PMID: 37904084 PMCID: PMC10617066 DOI: 10.1186/s12859-023-05542-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 10/20/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND A Raman spectroscopy method can quickly and accurately measure the concentration of ofloxacin in solution. This method has the advantages of accuracy and rapidity over traditional detection methods. However, the manual analysis methods for the collected Raman spectral data often ignore the nonlinear characteristics of the data and cannot accurately predict the concentration of the target sample. METHODS To address this drawback, this paper proposes a novel kernel-Huber loss function that combines the Huber loss function with the Gaussian kernel function. This function is used with an improved genetic algorithm-convolutional neural network (GA-CNN) to model and predict the Raman spectral data of different concentrations of ofloxacin in solution. In addition, the paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRU) models to conduct multiple experiments and use root mean square error (RMSE) and residual predictive deviation (RPD) as evaluation metrics. RESULTS The proposed method achieved an [Formula: see text] of 0.9989 on the test set data and improved by 3% over the traditional CNN. Multiple experiments were also conducted using RNN, LSTM, BiLSTM, and GRU models and evaluated their performance using RMSE, RPD, and other metrics. The results showed that the proposed method consistently outperformed these models. CONCLUSIONS This paper demonstrates the effectiveness of the proposed method for predicting the concentration of ofloxacin in solution based on Raman spectral data, in addition to discussing the advantages and limitations of the proposed method, and the study proposes a solution to the problem of deep learning methods for Raman spectral concentration prediction.
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Affiliation(s)
- Chenyu Ma
- School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113001, China
| | - Yuanbo Shi
- School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun, 113001, China.
| | - Yueyang Huang
- School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113001, China
| | - Gongwei Dai
- School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun, 113001, China
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Liu X, Wang K, Liu F, Zhao W, Liu J. 3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification. Cogn Neurodyn 2023; 17:1357-1380. [PMID: 37786651 PMCID: PMC10542086 DOI: 10.1007/s11571-022-09906-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 08/01/2022] [Accepted: 09/06/2022] [Indexed: 10/04/2023] Open
Abstract
Recently, deep learning-based methods have achieved meaningful results in the Motor imagery electroencephalogram (MI EEG) classification. However, because of the low signal-to-noise ratio and the various characteristics of brain activities among subjects, these methods lack a subject adaptive feature extraction mechanism. Another issue is that they neglect important spatial topological information and the global temporal variation trend of MI EEG signals. These issues limit the classification accuracy. Here, we propose an end-to-end 3D CNN to extract multiscale spatial and temporal dependent features for improving the accuracy performance of 4-class MI EEG classification. The proposed method adaptively assigns higher weights to motor-related spatial channels and temporal sampling cues than the motor-unrelated ones across all brain regions, which can prevent influences caused by biological and environmental artifacts. Experimental evaluation reveals that the proposed method achieved an average classification accuracy of 93.06% and 97.05% on two commonly used datasets, demonstrating excellent performance and robustness for different subjects compared to other state-of-the-art methods.In order to verify the real-time performance in actual applications, the proposed method is applied to control the robot based on MI EEG signals. The proposed approach effectively addresses the issues of existing methods, improves the classification accuracy and the performance of BCI system, and has great application prospects.
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Affiliation(s)
- Xiuling Liu
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002 China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, 071002 China
| | - Kaidong Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002 China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, 071002 China
| | - Fengshuang Liu
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002 China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding, 071002 China
| | - Wei Zhao
- College of Computer and Cyber Security, Hebei Normal University, Street, Shijiazhuang, 050024 China
| | - Jing Liu
- College of Computer and Cyber Security, Hebei Normal University, Street, Shijiazhuang, 050024 China
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Long T, Wan M, Jian W, Dai H, Nie W, Xu J. Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset. Front Hum Neurosci 2023; 17:1143027. [PMID: 37056962 PMCID: PMC10089123 DOI: 10.3389/fnhum.2023.1143027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/03/2023] [Indexed: 03/30/2023] Open
Abstract
IntroductionThe volume conduction effect and high dimensional characteristics triggered by the excessive number of channels of EEG cap-acquired signals in BCI systems can increase the difficulty of classifying EEG signals and the lead time of signal acquisition. We aim to combine transfer learning to decode EEG signals in the few-channel case, improve the classification performance of the motor imagery BCI system across subject cases, reduce the cost of signal acquisition performed by the BCI system, and improve the usefulness of the system.MethodsDataset2a from BCI CompetitionIV(2008) was used as Dataset1, and our team's self-collected dataset was used as Dataset2. Dataset1 acquired EEG signals from 9 subjects using a 22-channel device with a sampling frequency of 250 Hz. Dataset2 acquired EEG signals from 10 healthy subjects (8 males and 2 females; age distribution between 21-30 years old; mean age 25 years old) using an 8-channel system with a sampling frequency of 1000 Hz. We introduced EA in the data preprocessing process to reduce the signal differences between subjects and proposed VFB-RCSP in combination with RCSP and FBCSP to optimize the effect of feature extraction.ResultsExperiments were conducted on Dataset1 with EEG data containing only 8 channels and achieved an accuracy of 78.01 and a kappa coefficient of 0.54. The accuracy exceeded most of the other methods proposed in recent years, even though the number of channels used was significantly reduced. On Dataset 2, an accuracy of 59.77 and a Kappa coefficient of 0.34 were achieved, which is a significant improvement compared to other poorly improved classical protocols.DiscussionOur work effectively improves the classification of few-channel EEG data. It overcomes the dependence of existing algorithms on the number of channels, the number of samples, and the frequency band, which is significant for reducing the complexity of BCI models and improving the user-friendliness of BCI systems.
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Affiliation(s)
- Taixue Long
- The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Information Engineering School, Nanchang University, Nanchang, Jiangxi, China
| | - Min Wan
- The Second Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Wenjuan Jian
- Information Engineering School, Nanchang University, Nanchang, Jiangxi, China
- *Correspondence: Wenjuan Jian
| | - Honghui Dai
- Information Engineering School, Nanchang University, Nanchang, Jiangxi, China
| | - Wenbing Nie
- The Army Infantry College of PLA, Nanchang, Jiangxi, China
| | - Jianzhong Xu
- The Army Infantry College of PLA, Nanchang, Jiangxi, China
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A compact multi-branch 1D convolutional neural network for EEG-based motor imagery classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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6
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Guan S, Yuan Z, Wang F, Li J, Kang X, Lu B. Multi-class Motor Imagery Recognition of Single Joint in Upper Limb Based on Multi-domain Feature Fusion. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11185-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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7
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Rajesh Kumar DT, Mahalaxmi U, MM R, Bhatt DD. Optimization enabled deep residual neural network for motor imagery EEG signal classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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8
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Tao L, Cao T, Wang Q, Liu D, Bai O, Sun J. Application of self-adaptive multiple-kernel extreme learning machine to improve MI-BCI performance of subjects with BCI illiteracy. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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Dong E, Zhang H, Zhu L, Du S, Tong J. A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control. Cogn Neurodyn 2022; 16:1123-1133. [PMID: 36237403 PMCID: PMC9508306 DOI: 10.1007/s11571-021-09779-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/02/2021] [Accepted: 12/23/2021] [Indexed: 11/25/2022] Open
Abstract
In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.
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Affiliation(s)
- Enzeng Dong
- Tianjin Key Laboratory of Control Theory and Applications in Complicated Systems, Tianjin University of Technology, Tianjin, 300384 China
| | - Haoran Zhang
- Tianjin Key Laboratory of Control Theory and Applications in Complicated Systems, Tianjin University of Technology, Tianjin, 300384 China
| | - Lin Zhu
- China North Industries Group 210 Research Institute, Beijing, China
| | - Shengzhi Du
- Department of Electrical Engineering, Tshwane University of Technology, Pretoria, 0001 South Africa
| | - Jigang Tong
- Tianjin Key Laboratory of Control Theory and Applications in Complicated Systems, Tianjin University of Technology, Tianjin, 300384 China
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Yang B, Ma J, Qiu W, Zhang J, Wang X. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Signal analysis and classification of a novel active brain-computer interface based on four-category sequential coding. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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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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Spatial and Temporal Evolution Analysis of Industrial Green Technology Innovation Efficiency in the Yangtze River Economic Belt. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19116361. [PMID: 35681945 PMCID: PMC9180332 DOI: 10.3390/ijerph19116361] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 01/27/2023]
Abstract
As a fusion point of innovation-driven green development, green technology innovation has become an essential engine for green transformation and high-quality economic development of the Yangtze River Economic Belt. Based on the panel data of 110 cities in the Yangtze River Economic Belt from 2006 to 2020, this paper uses the super-SBM model to measure the efficiency of industrial green technology innovation. Then, the Dagum Gini coefficient and its subgroup decomposition method, kernel density estimation, and the spatial Markov chain will discuss the convergence characteristics and dynamic evolution law of industrial green technology innovation efficiency in the Yangtze River Economic Belt. The results indicate several key points. (1) On the whole, the industrial green innovation efficiency of the Yangtze River Economic Belt shows a trend of the “N” type, which increases slowly at first and then decreases and then increases, and shows a non-equilibrium feature of “east high and west low” in space. (2) The average GML index of industrial green technology innovation efficiency in the Yangtze River Economic Belt is greater than 1, and technological progress is the main driving force in promoting efficiency growth. (3) There are spatial and temporal differences in industrial green technological innovation efficiency in the Yangtze River Economic Belt. Interregional differences and hypervariable density are the primary sources of overall differences. (4) During the study period, the absolute difference in industrial green technology innovation efficiency among regions showed a trend of “expansion-reduction-expansion”, and the innovation efficiency gradually converged to a single equilibrium point. (5) The industrial green technology innovation efficiency transfer in the Yangtze River Economic Belt shows a specific spatial dependence. Accordingly, policy suggestions are put forward to further improve industrial green technological innovation in the Yangtze River Economic Belt.
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Tuncer T, Dogan S, Baygin M, Rajendra Acharya U. Tetromino pattern based accurate EEG emotion classification model. Artif Intell Med 2022; 123:102210. [PMID: 34998511 DOI: 10.1016/j.artmed.2021.102210] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022]
Abstract
Nowadays, emotion recognition using electroencephalogram (EEG) signals is becoming a hot research topic. The aim of this paper is to classify emotions of EEG signals using a novel game-based feature generation function with high accuracy. Hence, a multileveled handcrafted feature generation automated emotion classification model using EEG signals is presented. A novel textural features generation method inspired by the Tetris game called Tetromino is proposed in this work. The Tetris game is one of the famous games worldwide, which uses various characters in the game. First, the EEG signals are subjected to discrete wavelet transform (DWT) to create various decomposition levels. Then, novel features are generated from the decomposed DWT sub-bands using the Tetromino method. Next, the maximum relevance minimum redundancy (mRMR) features selection method is utilized to select the most discriminative features, and the selected features are classified using support vector machine classifier. Finally, each channel's results (validation predictions) are obtained, and the mode function-based voting method is used to obtain the general results. We have validated our developed model using three databases (DREAMER, GAMEEMO, and DEAP). We have attained 100% accuracies using DREAMER and GAMEEMO datasets. Furthermore, over 99% of classification accuracy is achieved for DEAP dataset. Thus, our developed emotion detection model has yielded the best classification accuracy rate compared to the state-of-the-art techniques and is ready to be tested for clinical application after validating with more diverse datasets. Our results show the success of the presented Tetromino pattern-based EEG signal classification model validated using three public emotional EEG datasets.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan, Turkey
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Wang Z, Chen H, Zhu J, Ding Z. Daily PM2.5 and PM10 forecasting using linear and nonlinear modeling framework based on robust local mean decomposition and moving window ensemble strategy. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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16
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Ali M, A. Abd El-Moghith I, N. El-Derini M, M. Darwish S. Intelligent Machine Learning Based EEG Signal Classification Model. COMPUTERS, MATERIALS & CONTINUA 2022; 71:1821-1835. [DOI: 10.32604/cmc.2022.021119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 08/23/2021] [Indexed: 09/02/2023]
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17
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A novel method to reduce the motor imagery BCI illiteracy. Med Biol Eng Comput 2021; 59:2205-2217. [PMID: 34674118 DOI: 10.1007/s11517-021-02449-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 09/18/2021] [Indexed: 10/20/2022]
Abstract
To reduce the motor imagery brain-computer interface (MI-BCI) illiteracy phenomenon and improve the classification accuracy, this paper proposed a novel method combining paradigm selection and Riemann distance classification. Firstly, a novel sensitivity-based paradigm selection (SPS) algorithm is designed for the optimization of classification to find the best classification pattern through a sensitive indicator. Then, a generalized Riemann minimum distance mean (GRMDM) classifier is proposed by introducing a weight factor to fuse the Log-Euclidean Metric classifier and the Riemannian Stein divergence classifier. The experimental results show that the proposed method achieves a better performance for multi-class motor imagery tasks. The average classification accuracy on the BCI competition IV dataset2a is 80.98%, which is 11.04% higher than Stein divergence classifier on the original two-class paradigm. Furthermore, the proposed method demonstrates its capacity on reducing MI-BCI illiteracy. Graphical abstract Here we investigate whether the BCI illiteracy phenomenon can be reduced through sensitivity-based paradigm selection (SPS) method and generalized Riemann minimum distance mean (GRMDM) classifier when performing motor imagery tasks.
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18
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A channel-mixing convolutional neural network for motor imagery EEG decoding and feature visualization. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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19
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Liu X, Lv L, Shen Y, Xiong P, Yang J, Liu J. Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification. J Neural Eng 2021; 18. [PMID: 33395676 DOI: 10.1088/1741-2552/abd82b] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/04/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Motor imagery (MI) electroencephalography (EEG) classification is regarded as a promising technology for brain--computer interface (BCI) systems, which help people to communicate with the outside world using neural activities. However, decoding human intent accurately is a challenging task because of its small signal-to-noise ratio and non-stationary characteristics. Methods that directly extract features from raw EEG signals ignores key frequency domain information. One of the challenges in MI classification tasks is finding a way to supplement the frequency domain information ignored by the raw EEG signal. APPROACH In this study, we fuse different models using their complementary characteristics to develop a multiscale space-time-frequency feature-guided multitask learning convolutional neural network (CNN) architecture. The proposed method consists of four modules: the space-time feature-based representation module, time-frequency feature-based representation module, multimodal fused feature-guided generation module, and classification module. The proposed framework is based on multitask learning. The four modules are trained using three tasks simultaneously and jointly optimised. RESULTS The proposed method is evaluated using three public challenge datasets. Through quantitative analysis, we demonstrate that our proposed method outperforms most state-of-the-art machine learning and deep learning techniques for EEG classification, thereby demonstrating the robustness and effectiveness of our method. Moreover, the proposed method is employed to realize control of robot based on EEG signal, verifying its feasibility in real-time applications. SIGNIFICANCE To the best of our knowledge, a deep CNN architecture that fuses different input cases, which have complementary characteristics, has not been applied to BCI tasks. Because of the interaction of the three tasks in the multitask learning architecture, our method can improve the generalisation and accuracy of subject-dependent and subject-independent methods with limited annotated data.
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Affiliation(s)
- Xiuling Liu
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Linyang Lv
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Yonglong Shen
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Peng Xiong
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, 071002, CHINA
| | - Jianli Yang
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Jing Liu
- Hebei Normal University, No.20 Road East. 2nd Ring South, Shijiazhuang, 050024, CHINA
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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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