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Qin Y, Zhang W, Tao X. TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1466-1476. [PMID: 38526885 DOI: 10.1109/tnsre.2024.3380595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG's spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.
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Zhang S, Wang Q, Zhang B, Liang Z, Zhang L, Li L, Huang G, Zhang Z, Feng B, Yu T. Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding. Front Neurosci 2023; 17:1292724. [PMID: 38027478 PMCID: PMC10654780 DOI: 10.3389/fnins.2023.1292724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information. Methods In this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers. Results We conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively. Conclusion The experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.
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Affiliation(s)
- Shaorong Zhang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China
| | - Qihui Wang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Benxin Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Zhen Liang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Li Zhang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Linling Li
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Gan Huang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China
| | - Zhiguo Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
| | - Bao Feng
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China
| | - Tianyou Yu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
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Dong Y, Wen X, Gao F, Gao C, Cao R, Xiang J, Cao R. Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion. Brain Sci 2023; 13:1109. [PMID: 37509039 PMCID: PMC10377689 DOI: 10.3390/brainsci13071109] [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: 06/17/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient's energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system.
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Affiliation(s)
- Yanqing Dong
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Xin Wen
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Fang Gao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Chengxin Gao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Ruochen Cao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Rui Cao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
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Wang Y, Cui F, Ding X, Yao Y, Li G, Gui G, Shen F, Li B. Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control. SENSORS 2022; 22:s22155802. [PMID: 35957360 PMCID: PMC9370865 DOI: 10.3390/s22155802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/23/2022] [Accepted: 07/30/2022] [Indexed: 11/28/2022]
Abstract
Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.
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Ai Q, Zhao M, Chen K, Zhao X, Ma L, Liu Q. Flexible coding scheme for robotic arm control driven by motor imagery decoding. J Neural Eng 2022; 19. [PMID: 35896097 DOI: 10.1088/1741-2552/ac84a9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 07/27/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain computer interface (BCI) technology is a new way of information exchange, which can effectively convert physiological signals into control instructions of machines. Due to its spontaneity and device-independence, motor imagery (MI) electroencephalography (EEG) signal is used as a common BCI signal source to achieve direct control of external devices. However, the generalization ability of current classification model of MI tasks is still limited. Moreover, the real time prototype is far from established in practice. APPROACH In order to solve these problems, this paper proposed an optimized neural network architecture based on our previous work. Firstly, the artifact components in MI-EEG signal are removed by using the threshold and threshold function related to the artifact removal evaluation index, and then the data is augmented by the empirical mode decomposition (EMD) algorithm. Furthermore, ensemble learning (EL) method and fine-tuning strategy in transfer learning (TL) are used to optimize the classification model. Finally, combined with the flexible binary encoding strategy, the EEG signal recognition results are mapped to the control commands of the robotic arm, which realizes the multiple degree of freedom control of the robotic arm. MAIN RESULTS The results show that EMD has an obvious data amount enhancement effect on small dataset, and the EL and TL can improve intra-subject and inter-subject model evaluation performance, respectively. The use of binary coding method realizes the expansion of control instructions, i.e., four kinds of MI-EEG signals are used to complete the control of 7 degrees of freedom of the robotic arm. SIGNIFICANCE Our work not only improves the classification accuracy of subject and the generality of classification model, but also extends the BCI control instruction set.
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Affiliation(s)
- Qingsong Ai
- School of Information Engineering, Wuhan University of Technology, No.122,Luoshi Road, Hongshan District, Wuhan, Wuhan, Hubei, 430070, CHINA
| | - Mengyuan Zhao
- School of Information Engineering, Wuhan University of Technology, No.122,Luoshi Road, Hongshan District, Wuhan, Wuhan, Hubei, 430070, CHINA
| | - Kun Chen
- School of Information Engineering, Wuhan University of Technology, No.122,Luoshi Road, Hongshan District, Wuhan, Wuhan, Hubei, 430070, CHINA
| | - Xuefei Zhao
- Wuhan University of Technology, No.122,Luoshi Road, Hongshan District, Wuhan, Wuhan, Hubei, 430070, CHINA
| | - Li Ma
- Wuhan University of Technology, No.122,Luoshi Road, Hongshan District, Wuhan, 430070, CHINA
| | - Quan Liu
- School of Information Engineering, Wuhan University of Technology, No.122,Luoshi Road, Hongshan District, Wuhan, Hubei, 430070, CHINA
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