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Jin J, Bai G, Xu R, Qin K, Sun H, Wang X, Cichocki A. A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces. J Neural Eng 2024; 21:036057. [PMID: 38885683 DOI: 10.1088/1741-2552/ad593b] [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: 12/14/2023] [Accepted: 06/17/2024] [Indexed: 06/20/2024]
Abstract
Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs.Approach.We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine.Main results.Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively.Significance.By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.
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Affiliation(s)
- Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China
| | - Guanglian Bai
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China
| | - Ren Xu
- Guger Technologies OG, Graz, Austria
| | - Ke Qin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China
| | - Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China
| | - Andrzej Cichocki
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
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李 湘, 王 丹, 张 柏, 范 超, 陈 佳, 许 萌, 陈 远. [A review on electroencephalogram based channel selection]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:398-405. [PMID: 38686423 PMCID: PMC11058489 DOI: 10.7507/1001-5515.202308034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 02/08/2024] [Indexed: 05/02/2024]
Abstract
The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.
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Affiliation(s)
- 湘喆 李
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - 丹 王
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - 柏雯 张
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - 超杰 范
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - 佳明 陈
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - 萌 许
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
| | - 远方 陈
- 北京工业大学 信息学部(北京 100124)Faculty of information Technology, Beijing University of Technology, Beijing 100124, P.R. China
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Liang W, Jin J, Xu R, Wang X, Cichocki A. Variance characteristic preserving common spatial pattern for motor imagery BCI. Front Hum Neurosci 2023; 17:1243750. [PMID: 38021234 PMCID: PMC10666778 DOI: 10.3389/fnhum.2023.1243750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space. Methods This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly. Results The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm. Discussion The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.
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Affiliation(s)
- Wei Liang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
- Shenzhen Research Institute of East China University of Science and Technology, Shenzhen, China
| | - Ren Xu
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
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Sun H, Jin J, Daly I, Huang Y, Zhao X, Wang X, Cichocki A. Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. J Neurosci Methods 2023; 399:109969. [PMID: 37683772 DOI: 10.1016/j.jneumeth.2023.109969] [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: 05/26/2023] [Revised: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 09/10/2023]
Abstract
Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.
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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, China
| | - Jing Jin
- 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 Science and Technology, Shen Zhen 518063, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Yitao Huang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xueqing Zhao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- RIKEN Brain Science Institute, Wako 351-0198, Japan; Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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Liang W, Jin J, Daly I, Sun H, Wang X, Cichocki A. Novel channel selection model based on graph convolutional network for motor imagery. Cogn Neurodyn 2023; 17:1283-1296. [PMID: 37786654 PMCID: PMC10542066 DOI: 10.1007/s11571-022-09892-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/03/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.
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Affiliation(s)
- Wei Liang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
- Shenzhen Research Institute of East China University of Technology, Shenzhen, 518063 China
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology, Moscow, Russia 143026
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
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Li MA, Ruan ZW. Decoding motor imagery with a simplified distributed dipoles model at source level. Cogn Neurodyn 2023; 17:445-457. [PMID: 37007206 PMCID: PMC10050617 DOI: 10.1007/s11571-022-09826-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 04/21/2022] [Accepted: 05/20/2022] [Indexed: 11/29/2022] Open
Abstract
Motor imagery (MI) based brain computer interface significantly oriented the development of neuro-rehabilitation, and the crucial issue is how to accurately detect the changes of cerebral cortex for MI decoding. The brain activity can be calculated based on the head model and observed scalp EEG, providing insights regarding cortical dynamics by using equivalent current dipoles with high spatial and temporal resolution. Now, all the dipoles within entire cortex or partial regions of interest are directly applied to data representation, this may make the key information weakened or lost, and it is worth studying how to choose the most important from numerous dipoles. In this paper, we devote to building a simplified distributed dipoles model (SDDM), which is combined with convolutional neural network (CNN), generating a MI decoding method at source level (called SDDM-CNN). First, all channels of raw MI-EEG signals are subdivided by a series of bandpass filters with width of 1 Hz, the average energies associated with any sub-band signals are calculated and ranked in a descending order to screen the top n sub-bands; then, the MI-EEG signals over each selected sub-band are mapped into source space by using EEG source imaging technology, and for each scout of neuroanatomical Desikan-Killiany partition, a centered dipole is selected as the most relevant dipole and put together to build a SDDM to reflect the neuroelectric activity of entire cerebral cortex; finally, the 4 dimensional (4D) magnitude matrix is constructed for each SDDM and fused into a novel data representation, which is further input to a well-designed 3DCNN with n parallel branches (nB3DCNN) to extract and classify the comprehensive features from time-frequency-space dimensions. Experiments are carried out on three public datasets, and the average ten-fold CV decoding accuracies achieve 95.09%, 97.98% and 94.53% respectively, and the statistical analysis is fulfilled by standard deviation, kappa value and confusion matrix. Experiment results suggest that it is beneficial to pick out the most sensitive sub-bands in sensor domain, and SDDM can sufficiently describe the dynamic changing of entire cortex, improving decoding performance while greatly reducing number of source signals. Also, nB3DCNN is capable of exploring spatial-temporal features from multi sub-bands.
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Affiliation(s)
- Ming-ai Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124 China
- Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124 China
| | - Zi-wei Ruan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 China
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Shi B, Yue Z, Yin S, Wang W, Yu H, Huang Z, Wang J. Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI. J Neural Eng 2022; 19. [PMID: 35772393 DOI: 10.1088/1741-2552/ac7d73] [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: 01/05/2022] [Accepted: 06/30/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Multi-channel EEG data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery-based BCI systems. Therefore, channel selection can improve BCI performance and contribute to user convenience. Additionally, cross-subject generalization is a key topic in the channel selection of motor imagery-based BCI. APPROACH In this study, an adaptive binary multi-objective harmony search (ABMOHS) algorithm is proposed to select the optimal set of channels. Furthermore, a new adaptive crosssubject generalization model (ACGM) is proposed. Three public motor imagery datasets were used to validate the effectiveness of the proposed method. MAIN RESULTS The Wilcoxon signed-rank test was performed on the test accuracies, and the results indicated that the ABMOHS method significantly outperformed all channels (p<0.001), the C3-Cz-C4 channels (p<0.001), and 20 channels (p<0.001) in the sensorimotor cortex. The ABMOHS algorithm based on Fisher's linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers greatly reduces the number of selected channels, especially for larger channel sizes (Dataset 2), and obtains a comparative classification performance. Although there was no significant difference in test classification performance between ABMOHS and non-dominated sorting genetic algorithm II (NSGA-II) when FLDA and SVM were used, ABMOHS required less computational time than NSGA-II. Furthermore, the number of channels obtained by ABMOHS algorithm were significantly smaller than those obtained by CSP-Rank and correlation-based channel selection algorithm (CCS). Additionally, the generalization of ACGM to untrained subjects shows that the mean test classification accuracy of ACGM created by a small sample of trained subjects is significantly better than that of Special-16 and Special-32. SIGNIFICANCE The proposed method can reduce the calibration time in the training phase and improve the practicability of MI-BCI.
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Affiliation(s)
- Bin Shi
- School of Mechanical Engineering, Xi'an Jiaotong University, Institute of Robotics and Intelligent System, Xi'an, 710049, CHINA
| | - Zan Yue
- Xi'an Jiaotong University, Institute of Robotics and Intelligent System, Xi'an, Shaanxi, 710049, CHINA
| | - Shuai Yin
- Xi'an Jiaotong University, Institute of Robotics and Intelligent System, Xi'an, Shaanxi, 710049, CHINA
| | - Weizhen Wang
- Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, No.28, West Xianning Road, Xi'an, Shaanxi, 710049, CHINA
| | - Haoyong Yu
- Department of Bioengineering Faculty of Engineering, National University of Singapore, 5 Engineering Drive 1, E6, National University of Singapore, Singapore, 117608, SINGAPORE
| | - Zhen Huang
- Panyu Center Hospital, Department of Rehabilitation Medicine, Guangzhou, 511400, CHINA
| | - Jing Wang
- Xian Jiaotong University, Institute of Robotics and Intelligent Systems, Xi'an, Shaanxi, 710049, CHINA
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Miniaturization for wearable EEG systems: recording hardware and data processing. Biomed Eng Lett 2022; 12:239-250. [PMID: 35692891 PMCID: PMC9168644 DOI: 10.1007/s13534-022-00232-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/06/2022] [Accepted: 05/12/2022] [Indexed: 12/05/2022] Open
Abstract
As more people desire at-home diagnosis and treatment for their health improvement, healthcare devices have become more wearable, comfortable, and easy to use. In that sense, the miniaturization of electroencephalography (EEG) systems is a major challenge for developing daily-life healthcare devices. Recently, because of the intertwined relationship between EEG recording and processing, co-research of EEG recording hardware and data processing has been emphasized for whole-in-one miniaturized EEG systems. This paper introduces miniaturization techniques in analog-front-end hardware and processing algorithms for such EEG systems. To miniaturize EEG recording hardware, various types of compact electrodes and mm-sized integrated circuits (IC) techniques including artifact rejection are studied to record accurate EEG signals in a much smaller manner. Active electrode and in-ear EEG technologies are also researched to make small-form-factor EEG measurement structures. Furthermore, miniaturization techniques for EEG processing are discussed including channel selection techniques that reduce the number of required electrode channel and hardware implementation of processing algorithms that simplify the EEG processing stage.
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Liu Y, Xu X, Zhou Y, Xu J, Dong X, Li X, Yin S, Wen D. Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information. Cogn Neurodyn 2021; 15:987-997. [PMID: 34790266 PMCID: PMC8572246 DOI: 10.1007/s11571-021-09682-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/28/2021] [Accepted: 04/19/2021] [Indexed: 01/06/2023] Open
Abstract
This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.
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Affiliation(s)
- Yijun Liu
- School of Science, Yanshan University, Qinhuangdao, China
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xiaodong Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Jian Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - Xiaoli Li
- The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shimin Yin
- Department of Neurology, The Rocket Force Hospital of Chinese People’s Liberation Army, Beijing, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
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Xiao R, Huang Y, Xu R, Wang B, Wang X, Jin J. Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI. Cogn Neurodyn 2021; 16:791-803. [PMID: 35847541 PMCID: PMC9279536 DOI: 10.1007/s11571-021-09752-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 09/27/2021] [Accepted: 10/24/2021] [Indexed: 11/29/2022] Open
Abstract
In the motor-imagery (MI) based brain computer interface (BCI), multi-channel electroencephalogram (EEG) is often used to ensure the complete capture of physiological phenomena. With the redundant information and noise, EEG signals cannot be easily converted into separable features through feature extraction algorithms. Channel selection algorithms are proposed to address the issue, in which the filtering technique is widely used with the advantages of low computational cost and strong practicability. In this study, we proposed several improved methods for filtering channel selection algorithm. Specifically, based on the coefficient of variation and inter-class distance, a novel channel classification method was designed, which divided channels into different categories based on their contribution to feature extraction process. Then a filtering channel selection algorithm was proposed according to the previous classification method. Moreover, a new testing framework for filtering channel selection algorithms was proposed, which can better reflect the generalization ability of the algorithm. Experimental results indicated that the proposed channel classification method is effective, and the proposed testing framework is better than the original one. Meanwhile, the proposed channel selection algorithm achieved the accuracy of 87.7% and 81.7% in two BCI competition datasets, respectively, which was superior to competing algorithms.
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Zhang X, Jin J, Li S, Wang X, Cichocki A. Evaluation of color modulation in visual P300-speller using new stimulus patterns. Cogn Neurodyn 2021; 15:873-886. [PMID: 34603548 DOI: 10.1007/s11571-021-09669-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/04/2021] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
Objective The stimulus color of P300-BCI systems has been successfully modified. However, the effects of different color combinations have not been widely investigated. In this study, we designed new stimulus patterns to evaluate the influence of color modulation on the BCI performance and waveforms of the evoked related potential (ERP).Methods Comparison was performed for three new stimulus patterns consisting of red face and colored block-shape, namely, red face with a white rectangle (RFW), red face with a blue rectangle (RFB), and red face with a red rectangle (RFR). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. Repeated-measures ANOVA and Bonferroni correction were applied for statistical analysis. Results The RFW pattern obtained the highest average online accuracy with 96.94%, and those of RFR and RFB patterns were 93.61% and of 92.22% respectively. Significant differences in online accuracy and information transfer rate (ITR) were found between RFW and RFR patterns (p < 0.05). Conclusion Compared with RFR and RFB patterns, RFW yielded the best performance in P300-BCI. These new stimulus patterns with different color combinations have considerable importance to BCI applications and user-friendliness.
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Affiliation(s)
- Xinru Zhang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Shurui Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xingyu Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (Skoltech), 121205 Moscow, Russia.,Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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Liu T, Xu Z, Cao L, Tan G. Evolutionary Multitasking-Based Multiobjective Optimization Algorithm for Channel Selection in Hybrid Brain Computer Interfacing Systems. Front Neurosci 2021; 15:749232. [PMID: 34675771 PMCID: PMC8523842 DOI: 10.3389/fnins.2021.749232] [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: 07/29/2021] [Accepted: 09/08/2021] [Indexed: 11/13/2022] Open
Abstract
Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.
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Affiliation(s)
- Tianyu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Zhixiong Xu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lei Cao
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Guowei Tan
- Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
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A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network. Cogn Neurodyn 2021; 16:379-389. [PMID: 35401871 PMCID: PMC8934809 DOI: 10.1007/s11571-021-09721-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 07/07/2021] [Accepted: 08/07/2021] [Indexed: 10/20/2022] Open
Abstract
The Common Spatial Pattern (CSP) algorithm is the most widely used method for decoding Electroencephalography (EEG) signals from motor imagery (MI) paradigm. However, due to the inter-subject variability, the CSP algorithm heavily relies on the selection of filter bands and extensive analytical processing time required to build an effective model, which has been a challenge in current research. In this paper, we propose a narrow filter bank CSP (NFBCSP) algorithm, which automatically determines the optimal narrow band for two-class motor imagery by band search tree, and a high-performance classification model dedicated to each subject can be obtained in a short time for online processing or further offline analysis. The optimal narrow band is combined with the CSP algorithm to extract the dynamic features in the EEG signals. For the multi-class motor imagery task, it is first transformed into multiple One-Versus-Rest (OVR) tasks and determines the corresponding optimal narrow bands. After extracting the features of each optimal narrow band separately, the Deep Convolutional Neural Network (DCNN) is used for the fusion of band features and classification of multi-class motor imagery. Finally, we verified our method using two different motor imagery datasets, the BCI-VR dataset with two classes of motor imagery and the BCI Competition IV dataset 2a with four classes of motor imagery. The experimental results show that the proposed method achieves an average classification accuracy of 86.43% for the two-class motor imagery task, and 76.87% for the four-class motor imagery task, which outperforms other recent methods. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09721-x.
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Miao Y, Chen S, Zhang X, Jin J, Xu R, Daly I, Jia J, Wang X, Cichocki A, Jung TP. BCI-Based Rehabilitation on the Stroke in Sequela Stage. Neural Plast 2020; 2020:8882764. [PMID: 33414824 PMCID: PMC7752268 DOI: 10.1155/2020/8882764] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/25/2020] [Accepted: 11/30/2020] [Indexed: 11/24/2022] Open
Abstract
Background Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function. Results The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study. Conclusions Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.
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Affiliation(s)
- Yangyang Miao
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinru Zhang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Ren Xu
- Guger Technologies OG, Austria
| | - Ian Daly
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex CO4 3SQ, UK
| | - Jie Jia
- Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- Skolkowo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia
- Systems Research Institute PAS, Warsaw, Poland
- Nicolaus Copernicus University (UMK), Torun, Poland
| | - Tzyy-Ping Jung
- Institute for Neural Computation and Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
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