1
|
Li Z, Tan X, Li X, Yin L. Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection. Med Biol Eng Comput 2024; 62:2961-2973. [PMID: 38724769 DOI: 10.1007/s11517-024-03103-1] [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: 09/11/2023] [Accepted: 04/19/2024] [Indexed: 09/07/2024]
Abstract
Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.
Collapse
Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, 066004, China
| | - Xiaohui Tan
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
| | - Xinyu Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
| | - Liyong Yin
- Department of Neurology, The First Hospital of Qinhuangdao, No. 258 Wenhua Road, Haigang District, Qinhuangdao, 066004, Hebei, People's Republic of China.
| |
Collapse
|
2
|
Zaman A, Kumar S, Shatabda S, Dehzangi I, Sharma A. SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification. Med Biol Eng Comput 2024; 62:2769-2783. [PMID: 38700613 DOI: 10.1007/s11517-024-03096-x] [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: 12/12/2023] [Accepted: 04/14/2024] [Indexed: 05/16/2024]
Abstract
Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners. To bridge this gap, we introduce SleepBoost, a transparent multi-level tree-based ensemble model specifically designed for ASSC. Our approach includes a crafted feature engineering block (FEB) that extracts 41 time and frequency domain features, out of which 23 are selected based on their high mutual information score (> 0.23). Uniquely, SleepBoost integrates three fundamental linear models into a cohesive multi-level tree structure, further enhanced by a novel reward-based adaptive weight allocation mechanism. Tested on the Sleep-EDF-20 dataset, SleepBoost demonstrates superior performance with an accuracy of 86.3%, F1-score of 80.9%, and Cohen kappa score of 0.807, outperforming leading DL models in ASSC. An ablation study underscores the critical role of our selective feature extraction in enhancing model accuracy and interpretability, crucial for clinical settings. This innovative approach not only offers a more transparent alternative to traditional DL models but also extends potential implications for monitoring and understanding sleep patterns in the context of neurodegenerative disorders. The open-source availability of SleepBoost's implementation at https://github.com/akibzaman/SleepBoost can further facilitate its accessibility and potential for widespread clinical adoption.
Collapse
Affiliation(s)
- Akib Zaman
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shiu Kumar
- School of Electrical & Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Swakkhar Shatabda
- Centre for Artificial Intelligence and Robotics (CAIR), United International University, Dhaka, Bangladesh
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, USA
- Center for Computational and Integrative Biology, Rutgers University, Camden, USA
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia
| |
Collapse
|
3
|
Chen D. Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification. Heliyon 2024; 10:e28235. [PMID: 38560116 PMCID: PMC10981046 DOI: 10.1016/j.heliyon.2024.e28235] [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: 11/04/2023] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 04/04/2024] Open
Abstract
Background Traditional Common Spatial Pattern (CSP) algorithms for Electroencephalogram (EEG) signal classification are sensitive to noise and can produce low accuracy in small sample datasets. New method To solve the problem, an improved Empirical Mode Decomposition (EMD) Bagging Regularized CSP (RCSP) algorithm is proposed. It filters EEG signals through improved EMD, inhibits high-frequency noise, retains effective information in the characteristic frequency band, and uses Bagging algorithm for data reconstruction. Feature extraction is performed with regularization of spatial patterns and Fisher linear discriminant analysis for feature classification. T-test is used for classification. Results The improved EMD Bagging RCSP algorithm has improved accuracy and robustness compared to CSP and its derivatives. The average classification rate is increased by about 6%, demonstrating the effectiveness and correctness of the proposed algorithm.Comparison with existing methods: The proposed algorithm outperforms CSP and its derivatives by retaining effective information and inhibiting high-frequency noise in small sample EEG datasets. Conclusions The proposed EMD Bagging RCSP algorithm provides a reliable and effective method for EEG signal classification and can be used in various applications, including brain-computer interfaces and clinical EEG diagnosis.
Collapse
Affiliation(s)
- Dongyi Chen
- College of Electrical Engineering and Automation Fuzhou University, NO.2, Wulong Jiangbei Avenue, Fuzhou University Town, Minhou, Fuzhou City, Fujian Province, China
| |
Collapse
|
4
|
Pirasteh A, Shamseini Ghiyasvand M, Pouladian M. EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients. Disabil Rehabil Assist Technol 2024:1-11. [PMID: 38400897 DOI: 10.1080/17483107.2024.2316312] [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: 08/07/2023] [Accepted: 02/03/2024] [Indexed: 02/26/2024]
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.
Collapse
Affiliation(s)
- Alireza Pirasteh
- Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | | | - Majid Pouladian
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| |
Collapse
|
5
|
Nguyen MTD, Phan Xuan NY, Pham BM, Do HTM, Phan TNM, Nguyen QTT, Duong AHL, Huynh VK, Hoang BDC, Ha HTT. Optimize temporal configuration for motor imagery-based multiclass performance and its relationship with subject-specific frequency. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2022.101141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
|
6
|
Hu H, Pu Z, Li H, Liu Z, Wang P. Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:8526. [PMID: 36366225 PMCID: PMC9658317 DOI: 10.3390/s22218526] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.
Collapse
Affiliation(s)
| | | | | | | | - Peng Wang
- Correspondence: ; Tel.: +86-10-6277-2007
| |
Collapse
|
7
|
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]
|
8
|
Arjun, Rajpoot AS, Panicker MR. Subject independent emotion recognition using EEG signals employing attention driven neural networks. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103547] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
|
9
|
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.
Collapse
|
10
|
Wankhade SB, Doye DD. Hybrid hunt-based deep convolutional neural network for emotion recognition using EEG signals. Comput Methods Biomech Biomed Engin 2022; 25:1311-1331. [PMID: 35098819 DOI: 10.1080/10255842.2021.2007889] [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: 11/29/2022]
Abstract
Emotion recognition from the electroencephalogram (EEG) signals is a recent trend as EEG generated directly from the human brain is considered an effective modality for recognizing emotions. Though there are many methods to address the challenge associated with the recognition, the research community still focuses on advanced methods, like deep learning and optimization, to acquire effective emotion recognition. Hence, this research focuses on developing a well-adapted emotion recognition model with the aid of an optimized deep convolutional neural network (Deep CNN). The significance of this research relies on the proposed hybrid hunt optimization, which engages in selecting the informative electrodes based on the neuronal activities and tuning the hyper-parameters of Deep CNN. Moreover, the frequency bands are analyzed, and frequency-based features are utilized for emotion recognition, which further boosts the recognition efficiency, increasing the significance of EEG as an accurate modality for recognizing emotions. The analysis is done using the DEAP and SEED-IV datasets based on performance parameters, such as accuracy, specificity and sensitivity, and the frequency bands. The accuracy of the proposed recognition model is 96.68% using the DEAP dataset concerning the training percentage and 95.89% using the SEED-IV dataset concerning the k-fold.
Collapse
Affiliation(s)
- Sujata Bhimrao Wankhade
- Computer Science and Engineering Department, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India
| | - Dharmpal Dronacharya Doye
- Department of Electronic and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India
| |
Collapse
|
11
|
Liu S, Wang X, Zhao L, Zhao J, Xin Q, Wang SH. Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1710-1721. [PMID: 32833640 DOI: 10.1109/tcbb.2020.3018137] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.
Collapse
|
12
|
Guan S, Li J, Wang F, Yuan Z, Kang X, Lu B. Discriminating three motor imagery states of the same joint for brain-computer interface. PeerJ 2021; 9:e12027. [PMID: 34513337 PMCID: PMC8395581 DOI: 10.7717/peerj.12027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/29/2021] [Indexed: 11/20/2022] Open
Abstract
The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.
Collapse
Affiliation(s)
- Shan Guan
- School of Mechanical Engineering, Northeast Electric Power University, Jilin, China
| | - Jixian Li
- School of Mechanical Engineering, Northeast Electric Power University, Jilin, China
| | - Fuwang Wang
- School of Mechanical Engineering, Northeast Electric Power University, Jilin, China
| | - Zhen Yuan
- School of Mechanical Engineering, Northeast Electric Power University, Jilin, China
| | - Xiaogang Kang
- School of Mechanical Engineering, Northeast Electric Power University, Jilin, China
| | - Bin Lu
- School of Mechanical Engineering, Northeast Electric Power University, Jilin, China
| |
Collapse
|
13
|
|
14
|
Kumar S, Tsunoda T, Sharma A. SPECTRA: a tool for enhanced brain wave signal recognition. BMC Bioinformatics 2021; 22:195. [PMID: 34078274 PMCID: PMC8170968 DOI: 10.1186/s12859-021-04091-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 03/21/2021] [Indexed: 12/31/2022] Open
Abstract
Background Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain–computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP). Results The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively.
Conclusions Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
Collapse
Affiliation(s)
- Shiu Kumar
- School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.,Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.,School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.,Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia
| |
Collapse
|
15
|
Singh A, Hussain AA, Lal S, Guesgen HW. A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface. SENSORS 2021; 21:s21062173. [PMID: 33804611 PMCID: PMC8003721 DOI: 10.3390/s21062173] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 01/16/2023]
Abstract
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.
Collapse
|
16
|
Kumar S, Sharma R, Sharma A. OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals. PeerJ Comput Sci 2021; 7:e375. [PMID: 33817023 PMCID: PMC7959638 DOI: 10.7717/peerj-cs.375] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
A human-computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In this work, we address the problem of selecting the appropriate filtering frequency band while also achieving a good system performance by proposing a frequency-based approach using long short-term memory network (LSTM) for recognizing different brain wave signals. Adaptive filtering using genetic algorithm is incorporated for a hybrid system utilizing common spatial pattern and LSTM network. The proposed method (OPTICAL+) achieved an overall average classification error rate of 30.41% and a kappa coefficient value of 0.398, outperforming the state-of-the-art methods. The proposed OPTICAL+ predictor can be used to develop improved HCI systems that will aid in neurorehabilitation and may also be beneficial for sleep stage classification and seizure detection.
Collapse
Affiliation(s)
- Shiu Kumar
- School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji
| | - Ronesh Sharma
- School of Electrical and Electronic Engineering, Fiji National University, Suva, Fiji
| | - Alok Sharma
- STEMP, University of the South Pacific, Suva, Fiji
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| |
Collapse
|
17
|
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.
Collapse
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;
| |
Collapse
|
18
|
An adaptive multi-domain feature joint optimization framework based on composite kernels and ant colony optimization for motor imagery EEG classification. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101994] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
19
|
Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:1981728. [PMID: 32765639 PMCID: PMC7387988 DOI: 10.1155/2020/1981728] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/30/2020] [Accepted: 02/20/2020] [Indexed: 11/19/2022]
Abstract
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feature extraction, common spatial pattern (CSP) is one of the most frequently used algorithms. However, in order to extract the optimal CSP features, prior knowledge and complex parameter adjustment are often required. Convolutional neural network (CNN) is one of the most popular deep learning models at present. Within CNN, feature learning and pattern classification are carried out simultaneously during the procedure of iterative updating of network parameters; thus, it can remove the complicated manual feature engineering. In this paper, we propose a novel deep learning methodology which can be used for spatial-frequency feature learning and classification of motor imagery EEG. Specifically, a multilayer CNN model is designed according to the spatial-frequency characteristics of MI EEG signals. An experimental study is carried out on two MI EEG datasets (BCI competition III dataset IVa and a self-collected right index finger MI dataset) to validate the effectiveness of our algorithm in comparison with several closely related competing methods. Superior classification performance indicates that our proposed method is a promising pattern recognition algorithm for MI-based BCI system.
Collapse
|
20
|
Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn 2020; 15:141-156. [PMID: 33786085 DOI: 10.1007/s11571-020-09608-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 05/09/2020] [Accepted: 06/13/2020] [Indexed: 11/27/2022] Open
Abstract
Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.
Collapse
|
21
|
Xu J, Zheng H, Wang J, Li D, Fang X. Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3496. [PMID: 32575798 PMCID: PMC7349253 DOI: 10.3390/s20123496] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 06/05/2020] [Accepted: 06/18/2020] [Indexed: 01/16/2023]
Abstract
Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.
Collapse
Affiliation(s)
- Jiacan Xu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| | - Hao Zheng
- School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China;
| | - Jianhui Wang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| | - Donglin Li
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| | - Xiaoke Fang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| |
Collapse
|
22
|
A new BAT optimization algorithm based feature selection method for electrocardiogram heartbeat classification using empirical wavelet transform and Fisher ratio. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01128-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
23
|
Kumar S, Sharma A, Tsunoda T. Brain wave classification using long short-term memory network based OPTICAL predictor. Sci Rep 2019; 9:9153. [PMID: 31235800 PMCID: PMC6591300 DOI: 10.1038/s41598-019-45605-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 06/07/2019] [Indexed: 11/09/2022] Open
Abstract
Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL .
Collapse
Affiliation(s)
- Shiu Kumar
- The University of the South Pacific, Suva, Fiji. .,Fiji National University, Suva, Fiji.
| | - Alok Sharma
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD-4111, Australia. .,Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan. .,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan. .,The University of the South Pacific, Suva, Fiji.
| | - Tatsuhiko Tsunoda
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.,Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan.,CREST, JST, Tokyo, 102-8666, Japan
| |
Collapse
|
24
|
Park Y, Chung W. Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1378-1388. [PMID: 31199263 DOI: 10.1109/tnsre.2019.2922713] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a novel feature extraction approach for motor imagery classification overcoming the weakness of conventional common spatial pattern (CSP) methods, especially for small sample settings. We consider local CSPs generated from individual channels and their neighbors (termed "local regions") rather than a global CSP generated from all channels. The novelty is to select a few good local regions using interquartile range (IQR) or an "above the mean" rule based on variance ratio dispersion score (VRDS) and inter-class feature distance (ICFD); instead of computationally expensive cross-validation method. Furthermore, we develop frequency optimization using filter banks by extending the VRDS and ICFD to frequency-optimized local CSPs. The proposed methods are tested on three publicly available brain-computer interface (BCI) datasets: BCI competition III dataset IVa, BCI competition IV dataset I, and BCI competition IV dataset IIb. The proposed method exhibits substantially improved classification accuracy compared to recent related motor imagery (MI) classification methods.
Collapse
|
25
|
Luo J, Wang J, Xu R, Xu K. Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification. J Neurosci Methods 2019; 323:98-107. [PMID: 31141703 DOI: 10.1016/j.jneumeth.2019.05.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Motor imagery classification, an important branch of brain-computer interface (BCI), recognizes the intention of subjects to control external auxiliary equipment. Therefore, EEG-based motor imagery classification has received increasing attention in the fields of neuroscience. The common spatial pattern (CSP) algorithm has recently achieved great success in motor imagery classification. However, varying discriminative frequency bands and few-channel EEG limit the performance of CSP. NEW METHOD A class discrepancy-guided sub-band filter-based CSP (CDFCSP) algorithm is proposed to automatically recognize and augment the discriminative frequency bands for CSP algorithms. Specifically, a priori knowledge and templates obtained from the training set were applied as the design guidelines of the class discrepancy-guided sub-band filter (CDF). Second, a filter bank CSP was used to extract features from EEG traces filtered by the CDF. Finally, the CSP features of multiple frequency bands were leveraged to train linear support vector machine classifier and generate prediction. RESULTS BCI competition IV datasets 2a and 2b, which include EEGs from 18 subjects, were used to validate the performance improvement provided by the CDF. Student's t-tests of the CDFCSP versus the filter bank CSP without the CDF showed that the performance improvement was significant (i.e., p-values of 0.040 and 0.032 for the ratio and normalization mode CDFCSP, respectively). COMPARISON WITH EXISTING METHOD(S) The experiments show that the proposed CDFCSP improves the CSP algorithm and outperforms the other state-of-the-art algorithms evaluated in this paper. CONCLUSIONS The increased performance of the proposed CDFCSP algorithm can promote the application of BCI systems.
Collapse
Affiliation(s)
- Jing Luo
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, China; Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an University of Technology, Xi'an, Shaanxi, China.
| | - Jie Wang
- State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Rong Xu
- Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design, Xi'an, Shaanxi, China
| | - Kailiang Xu
- School of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi, China
| |
Collapse
|
26
|
Reconfiguration patterns of large-scale brain networks in motor imagery. Brain Struct Funct 2019; 224:553-566. [DOI: 10.1007/s00429-018-1786-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 10/26/2018] [Indexed: 10/27/2022]
|
27
|
Luo TJ, Zhou CL, Chao F. Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. BMC Bioinformatics 2018; 19:344. [PMID: 30268089 PMCID: PMC6162908 DOI: 10.1186/s12859-018-2365-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 09/10/2018] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. METHODS Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. RESULTS Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. CONCLUSION By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.
Collapse
Affiliation(s)
- Tian-jian Luo
- Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005 China
| | - Chang-le Zhou
- Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005 China
| | - Fei Chao
- Department of Cognitive Science, School of Information Science and Engineering, Xiamen University, 422 Siming South Road, Siming District, Xiamen, 361005 China
- Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth, Wales, SY23 3DB UK
| |
Collapse
|