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Yan S, Hu Y, Zhang R, Qi D, Hu Y, Yao D, Shi L, Zhang L. Multilayer network-based channel selection for motor imagery brain-computer interface. J Neural Eng 2024; 21:016029. [PMID: 38295419 DOI: 10.1088/1741-2552/ad2496] [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: 08/17/2023] [Accepted: 01/31/2024] [Indexed: 02/02/2024]
Abstract
Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.
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Affiliation(s)
- Shaoting Yan
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yuxia Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Rui Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Daowei Qi
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Yubo Hu
- The No.3 Provincial People's Hospital of Henan Province, Zhengzhou, People's Republic of China
| | - Dezhong Yao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, People's Republic of China
- Beijing National Research Center for Information Science and Technology, Beijing, People's Republic of China
| | - Lipeng Zhang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, People's Republic of China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, People's Republic of China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, People's Republic of China
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Wang H, Yua H, Wang H. EEG_GENet: A feature-level graph embedding method for motor imagery classification based on EEG signals. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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EEG based cognitive task classification using multifractal detrended fluctuation analysis. Cogn Neurodyn 2021; 15:999-1013. [PMID: 34790267 DOI: 10.1007/s11571-021-09684-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 03/24/2021] [Accepted: 05/12/2021] [Indexed: 10/20/2022] Open
Abstract
Locating cognitive task states by measuring changes in electrocortical activity due to various attentional and sensory-motor changes, has been in research interest since last few decades. In this paper, different cognitive states while performing various attentional and visuo-motor coordination tasks, are classified using electroencephalogram (EEG) signal. A non-linear time-series method, multifractal detrended fluctuation analysis (MFDFA) , is applied on respective EEG signal for features. Using MFDFA based features a multinomial classification is achieved. Nine channel EEG signal was recorded for 38 young volunteers (age: 25 ± 5 years, 30 male and 8 female), during six consecutive tasks. First three tasks are related to increasing levels of selective focus vision; next three are reflex and response based computer tasks. Total of 90 features (ten features from each of nine channel) were extracted from Hurst and singularity exponents of MFDFA on EEG signals. After feature selection, a multinomial classifier of six classes using two methods: support vector machine (SVM) and decision tree classifier (DTC). An accuracy of 96.84% using SVM and 92.49% using DTC was achieved.
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Menicucci D, Di Gruttola F, Cesari V, Gemignani A, Manzoni D, Sebastiani L. Task-independent Electrophysiological Correlates of Motor Imagery Ability from Kinaesthetic and Visual Perspectives. Neuroscience 2020; 443:176-187. [PMID: 32736068 DOI: 10.1016/j.neuroscience.2020.07.038] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/20/2020] [Accepted: 07/21/2020] [Indexed: 11/19/2022]
Abstract
Motor imagery (MI) ability is highly subjective, as indicated by the individual scores of the MIQ-3 questionnaire, and poor imagers compensate for the difficulty in performing MI with larger cerebral activations, as demonstrated by MI studies involving hands/limbs. In order to identify general, task-independent MI ability correlates, 16 volunteers were stratified with MIQ-3. The scores in the kinaesthetic (K) and 1st-person visual (V) perspectives were associated with EEG patterns obtained during K-MI and V-MI of the same complex MIQ-3 movements during these MI tasks (Spearman's correlation, significance at <0.05, SnPM corrected). EEG measures were relative to rest (relaxation, closed eyes), and based on six electrode clusters both for band spectral content and connectivity (Granger causality). Lower K-MI ability was associated with greater theta decreases during tasks in fronto-central clusters and greater inward information flow to prefrontal clusters for theta, high alpha and beta bands. On the other hand, power band relative decreases were associated with V-MI ability in fronto-central clusters for low alpha and left fronto-central and both centro-parietal clusters for beta bands. The results thus suggest different computational mechanisms for MI-V and MI-K. The association between low alpha/beta desynchronization and V-MIQ scores and between theta changes and K-MIQ scores suggest a cognitive effort with greater cerebral activation in participants with lower V-MI ability. The association between information flow to prefrontal hub and K-MI ability suggest the need for a continuous update of information to support MI-related executive functions in subjects with poor K-MI ability.
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Identifying motor imagery activities in brain computer interfaces based on the intelligent selection of most informative timeframe. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-2020-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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Ge S, Jiang Y, Wang P, Wang H, Zheng W. Training -Free Steady-State Visual Evoked Potential Brain-Computer Interface Based on Filter Bank Canonical Correlation Analysis and Spatiotemporal Beamforming Decoding. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1714-1723. [PMID: 31403435 DOI: 10.1109/tnsre.2019.2934496] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A brain-computer interface (BCI) provides a novel non-muscular communication pathway for individuals with severe neuromuscular diseases. BCI systems based on steady-state visual evoked potentials (SSVEPs) have high classification accuracy, information transfer rate, and signal-to-noise ratio, giving them high research and application value. However, SSVEP-based BCI has several limitations in real-world applications. The main challenge is how to reduce or eliminate the need for a dedicated training process while maintaining high classification accuracy. Filter bank canonical correlation analysis (FBCCA) is a powerful and widely used feature extraction method for SSVEP-based BCI systems. However, the reference signals of FBCCA are fixed-frequency sine-cosine waves, which makes it difficult to accurately describe the complex, mutative, and individually different physiological SSVEPs. Therefore, there is huge room for improvement in classification performance based on the FBCCA method. In contrast, although spatiotemporal beamforming (BF) detects SSVEPs with high accuracy, it needs an additional training process, which limits its application. In this study, we propose a bimodal decoding algorithm (FBCCA+BF), which combines the advantages of the training-free classification of FBCCA and the data-driven and adaptive features of BF. Six-channel SSVEP data corresponding to eight targets measured from 15 subjects were used to test the effectiveness of three different CCA-based methods, BF, and our proposed FBCCA+BF methods. It was found that the classification accuracies for BF and FBCCA+BF are 95.6% and 92.2%, respectively, which are significantly higher than the other CCA-based methods. Notably, both BF and FBCCA+BF obtain state-of-the-art performance, but FBCCA+BF does this without the need for a dedicated training process. Therefore, we conclude that our proposed FBCCA+BF method provides a training-free and high-accuracy approach for SSVEP-based BCIs.
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