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Shi B, Yue Z, Yin S, Zhao J, Wang J. Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding. Front Hum Neurosci 2023; 17:1292428. [PMID: 38130433 PMCID: PMC10733485 DOI: 10.3389/fnhum.2023.1292428] [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: 09/12/2023] [Accepted: 11/10/2023] [Indexed: 12/23/2023] Open
Abstract
Background Brain-computer interface (BCI) systems based on motor imagery (MI) have been widely used in neurorehabilitation. Feature extraction applied by the common spatial pattern (CSP) is very popular in MI classification. The effectiveness of CSP is highly affected by the frequency band and time window of electroencephalogram (EEG) segments and channels selected. Objective In this study, the multi-domain feature joint optimization (MDFJO) based on the multi-view learning method is proposed, which aims to select the discriminative features enhancing the classification performance. Method The channel patterns are divided using the Fisher discriminant criterion (FDC). Furthermore, the raw EEG is intercepted for multiple sub-bands and time interval signals. The high-dimensional features are constructed by extracting features from CSP on each EEG segment. Specifically, the multi-view learning method is used to select the optimal features, and the proposed feature sparsification strategy on the time level is proposed to further refine the optimal features. Results Two public EEG datasets are employed to validate the proposed MDFJO method. The average classification accuracy of the MDFJO in Data 1 and Data 2 is 88.29 and 87.21%, respectively. The classification result of MDFJO was significantly better than MSO (p < 0.05), FBCSP32 (p < 0.01), and other competing methods (p < 0.001). Conclusion Compared with the CSP, sparse filter band common spatial pattern (SFBCSP), and filter bank common spatial pattern (FBCSP) methods with channel numbers 16, 32 and all channels as well as MSO, the MDFJO significantly improves the test accuracy. The feature sparsification strategy proposed in this article can effectively enhance classification accuracy. The proposed method could improve the practicability and effectiveness of the BCI system.
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Affiliation(s)
- Bin Shi
- Xi’an Research Institute of High-Technology, Xi’an, Shaanxi, China
| | - Zan Yue
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Shuai Yin
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Junyang Zhao
- Xi’an Research Institute of High-Technology, Xi’an, Shaanxi, China
| | - Jing Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
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Mwata-Velu T, Niyonsaba-Sebigunda E, Avina-Cervantes JG, Ruiz-Pinales J, Velu-A-Gulenga N, Alonso-Ramírez AA. Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:4164. [PMID: 37112504 PMCID: PMC10145994 DOI: 10.3390/s23084164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Nowadays, Brain-Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT's public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems' requirements, dealing with short processing times and reliable classification accuracy.
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Affiliation(s)
- Tat’y Mwata-Velu
- Centro de Investigación en Computación, Instituto Politécnico Nacional (CIC-IPN), Avenida Juan de Dios Bátiz Esquina Miguel Othón de Mendizábal Colonia Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero, Ciudad de Mexico C.P. 07738, Mexico
- Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo; (E.N.-S.); (J.R.-P.)
- Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico;
| | - Edson Niyonsaba-Sebigunda
- Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo; (E.N.-S.); (J.R.-P.)
| | - Juan Gabriel Avina-Cervantes
- Telematics and Digital Signal Processing Research Groups (CAs), Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico;
| | - Jose Ruiz-Pinales
- Institut Supérieur Pédagogique Technique de Kinshasa (I.S.P.T.-KIN), Av. de la Science 5, Gombe, Kinshasa 3287, Democratic Republic of the Congo; (E.N.-S.); (J.R.-P.)
| | - Narcisse Velu-A-Gulenga
- Institut Supérieur Pédagogique de Kikwit (I.S.P. KIKWIT), Av Nzundu 2, Com. Lukolela, Kikwit 8211, Democratic Republic of the Congo
| | - Adán Antonio Alonso-Ramírez
- Instituto Tecnológico Nacional de México en Celaya (TecNM-Celaya), Av. Antonio García Cubas Pte 600, Celaya C.P. 38010, Guanajuato, Mexico;
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Collazos-Huertas DF, Álvarez-Meza AM, Cárdenas-Peña DA, Castaño-Duque GA, Castellanos-Domínguez CG. Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity. SENSORS (BASEL, SWITZERLAND) 2023; 23:2750. [PMID: 36904950 PMCID: PMC10007181 DOI: 10.3390/s23052750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/19/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.
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Affiliation(s)
| | | | | | - Germán Albeiro Castaño-Duque
- Cultura de la Calidad en la Educación Research Group, Universidad Nacional de Colombia, Manizales 170003, Colombia
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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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Shi B, Chen X, Yue Z, Zeng F, Yin S, Wang B, Wang J. Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification. Front Comput Neurosci 2022; 16:1004301. [PMID: 36589278 PMCID: PMC9801329 DOI: 10.3389/fncom.2022.1004301] [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: 07/27/2022] [Accepted: 11/23/2022] [Indexed: 12/23/2022] Open
Abstract
Background Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding. Objective This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction. Methods The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method. Results The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time. Conclusion These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.
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Affiliation(s)
- Bin Shi
- Xi’an Research Institute of High-Technology, Xi’an, Shaanxi, China
| | - Xiaokai Chen
- Rehabilitation Medical Center, Huizhou Third People’s Hospital, Huizhou, China
| | - Zan Yue
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- iHarbour Academy of Frontier Equipment (iAFE), Xi’an, China
| | - Feixiang Zeng
- Rehabilitation Medical Center, Huizhou Third People’s Hospital, Huizhou, China
| | - Shuai Yin
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- iHarbour Academy of Frontier Equipment (iAFE), Xi’an, China
| | - Benguo Wang
- Department of Rehabilitation Medicine, Longgang District People’s Hospital of Shenzhen, Shenzhen, China
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of The Chinese University of Hong Kong, Shenzhen, China
| | - Jing Wang
- Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- iHarbour Academy of Frontier Equipment (iAFE), Xi’an, China
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Zhang Y, Zhou T, Wu W, Xie H, Zhu H, Zhou G, Cichocki A. Improving EEG Decoding via Clustering-Based Multitask Feature Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3587-3597. [PMID: 33556021 DOI: 10.1109/tnnls.2021.3053576] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution and, hence, can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multitask feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multitask learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses. We then train a linear support vector machine with the optimized features for EEG pattern decoding. Extensive experimental studies are conducted on three EEG data sets to validate the effectiveness of our algorithm in comparison with other state-of-the-art approaches. The improved experimental results demonstrate the outstanding superiority of our algorithm, suggesting its prominent performance for EEG pattern decoding in BCI applications.
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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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Fujiwara Y, Ushiba J. Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain. Front Comput Neurosci 2022; 16:882290. [PMID: 35669388 PMCID: PMC9165810 DOI: 10.3389/fncom.2022.882290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022] Open
Abstract
Concomitant with the development of deep learning, brain-computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested via within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model via subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.
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Affiliation(s)
- Yosuke Fujiwara
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
- Information Services International-Dentsu, Ltd., Tokyo, Japan
| | - Junichi Ushiba
- Faculty of Science and Technology, Keio University, Yokohama, Japan
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Värbu K, Muhammad N, Muhammad Y. Past, Present, and Future of EEG-Based BCI Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:3331. [PMID: 35591021 PMCID: PMC9101004 DOI: 10.3390/s22093331] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/05/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.
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Affiliation(s)
- Kaido Värbu
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia;
| | - Naveed Muhammad
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia;
| | - Yar Muhammad
- Department of Computing & Games, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK;
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Abstract
OBJECTIVE Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. APPROACH Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of the brain. MAIN RESULTS (1) EEGG was more robust than typical "CSP+" algorithms for the low-quality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. SIGNIFICANCE EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.
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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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Yan Y, Zhou H, Huang L, Cheng X, Kuang S. A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification. Front Neurosci 2021; 15:657540. [PMID: 34539326 PMCID: PMC8440963 DOI: 10.3389/fnins.2021.657540] [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: 01/23/2021] [Accepted: 07/27/2021] [Indexed: 11/17/2022] Open
Abstract
Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain-computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.
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Affiliation(s)
- Yuxin Yan
- The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Haifeng Zhou
- College of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Lixin Huang
- The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China
| | - Xiao Cheng
- Applied Technology College of Soochow University, Suzhou, China
| | - Shaolong Kuang
- College of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
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Rahman MKM, Haque T. Classification of motor imagery using a time-localised approach. J Med Eng Technol 2021; 45:361-374. [PMID: 33847221 DOI: 10.1080/03091902.2021.1906966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Brain-computer interface (BCI) is getting increasing attention where classification of motor imagery (MI) using electroencephalography (EEG) signal plays a vital role. In traditional EEG-based BCI setup, after applying pre-processing like band-pass filtering and spatial filtering, features are extracted and are fed to the classifier. However, most of the traditional features are extracted from a single time window, which is usually the full-time frame of a cue-based MI signal. Such features are usually statistical characteristics like log-variance of the whole-time signal. Thus, the information, which is localised in time and crucial for subject-specific MI classification, is not best captured. In this work, a new time-localised approach is proposed where multiple time windows are used for feature extraction. We have developed a number of feature representations using those time windows. Our experimental results corroborate that the proposed approach can achieve higher accuracy of classification when compared to methods using conventional features using the same platform.
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Affiliation(s)
- M K M Rahman
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Tohfa Haque
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
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