A coarse-to-fine adaptive spatial–temporal graph convolution network with residuals for motor imagery decoding from the same limb
•A novel coarse-to-fine classification approach is proposed to decode MI tasks of the same upper limb.•A adaptive spatial–temporal graph convolutional network with residuals approach is proposed to extract spatial–temporal features.•Various classification models are applied to different classificati...
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| Published in: | Biomedical signal processing and control Vol. 90; p. 105885 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.04.2024
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| ISSN: | 1746-8094, 1746-8108 |
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| Abstract | •A novel coarse-to-fine classification approach is proposed to decode MI tasks of the same upper limb.•A adaptive spatial–temporal graph convolutional network with residuals approach is proposed to extract spatial–temporal features.•Various classification models are applied to different classification stages, and each model is specifically designed according to the feature differences among categories.•The proposed approach obtains better results than classical approaches.
In the field of Brain Computer Interface (BCI) technology, Motor Imagery (MI) plays an important role as a paradigm. One of the primary focuses of this research area lies in exploring the MI of various upper limbs. Decoding MI signals from distinct joints within the same limb poses more intricate challenges in comparison to decoding MI signals originating from different upper limbs. In order to explore more efficient decoding methods, we propose a novel coarse-to-fine classification approach to investigate categorical decoding across three tasks, namely ‘rest’, ‘hand’, and ‘elbow’. This approach consists of two classification stages performed from coarse to fine. In the coarse classification stage, in order to capture the features of both resting state and the moving state in the temporal domain, the EEGNet network with temporal domain convolution is used to extract temporal domain features and classify the original samples into categories of ‘rest’ and ‘move’ (‘hand’, ‘elbow’). In the fine classification stage, the samples of ‘move’ category are segmented by time to form a graph sequence. Then, an adaptive spatial–temporal graph convolutional network with residuals is utilized to extract both temporal and spatial domains’ features from the graph sequence. The proposed algorithm has been validated experimentally on the MI-2 dataset and compared with contemporary methods. Its classification performance is quantified by the average accuracy which achieves a value of 72.21%. Extensive experimental results indicate that the novel coarse-to-fine classification approach is superior to the single classification approach. |
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| AbstractList | •A novel coarse-to-fine classification approach is proposed to decode MI tasks of the same upper limb.•A adaptive spatial–temporal graph convolutional network with residuals approach is proposed to extract spatial–temporal features.•Various classification models are applied to different classification stages, and each model is specifically designed according to the feature differences among categories.•The proposed approach obtains better results than classical approaches.
In the field of Brain Computer Interface (BCI) technology, Motor Imagery (MI) plays an important role as a paradigm. One of the primary focuses of this research area lies in exploring the MI of various upper limbs. Decoding MI signals from distinct joints within the same limb poses more intricate challenges in comparison to decoding MI signals originating from different upper limbs. In order to explore more efficient decoding methods, we propose a novel coarse-to-fine classification approach to investigate categorical decoding across three tasks, namely ‘rest’, ‘hand’, and ‘elbow’. This approach consists of two classification stages performed from coarse to fine. In the coarse classification stage, in order to capture the features of both resting state and the moving state in the temporal domain, the EEGNet network with temporal domain convolution is used to extract temporal domain features and classify the original samples into categories of ‘rest’ and ‘move’ (‘hand’, ‘elbow’). In the fine classification stage, the samples of ‘move’ category are segmented by time to form a graph sequence. Then, an adaptive spatial–temporal graph convolutional network with residuals is utilized to extract both temporal and spatial domains’ features from the graph sequence. The proposed algorithm has been validated experimentally on the MI-2 dataset and compared with contemporary methods. Its classification performance is quantified by the average accuracy which achieves a value of 72.21%. Extensive experimental results indicate that the novel coarse-to-fine classification approach is superior to the single classification approach. |
| ArticleNumber | 105885 |
| Author | Yuan, Jie Zhu, Lei Huang, Aiai Zhang, Jianhai |
| Author_xml | – sequence: 1 givenname: Lei surname: Zhu fullname: Zhu, Lei email: zhulei@hdu.edu.cn organization: School of Automation, Hangzhou Dianzi University, Hangzhou 310000, China – sequence: 2 givenname: Jie surname: Yuan fullname: Yuan, Jie organization: School of Automation, Hangzhou Dianzi University, Hangzhou 310000, China – sequence: 3 givenname: Aiai surname: Huang fullname: Huang, Aiai organization: School of Automation, Hangzhou Dianzi University, Hangzhou 310000, China – sequence: 4 givenname: Jianhai surname: Zhang fullname: Zhang, Jianhai organization: School of Computer Science, Hangzhou Dianzi University, Hangzhou 310000, China |
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| Cites_doi | 10.1109/TNSRE.2019.2953121 10.1109/TNSRE.2016.2601240 10.1007/s11517-008-0345-8 10.1109/TBME.2013.2248153 10.1007/s40815-016-0259-9 10.5539/gjhs.v10n11p66 10.1109/TNSRE.2022.3154369 10.1371/journal.pone.0121896 10.1038/s41597-020-0535-2 10.1016/j.mayocp.2011.12.008 10.1109/TBME.2007.897815 10.1007/s12559-017-9533-x 10.1109/ICCE-Asia49877.2020.9276983 10.1609/aaai.v32i1.12328 10.1016/j.neucom.2017.09.030 10.1142/S1793351X16500045 10.1016/j.medengphy.2011.11.001 10.1109/TNSRE.2008.926694 10.1007/978-3-540-88906-9_42 10.1109/IMCCC.2015.156 10.1371/journal.pone.0182578 10.1371/journal.pone.0188293 10.1016/j.clinph.2007.04.019 10.1109/LSP.2021.3049683 10.1016/j.jneumeth.2003.10.009 10.1109/TNSRE.2017.2721116 10.1016/j.pmrj.2014.01.006 10.1088/1741-2552/aba7cd 10.1177/1550059414522229 10.1145/2816839.2816845 10.1371/journal.pone.0174161 10.1109/5.939829 10.1007/s10044-019-00860-w 10.1109/TAMD.2015.2431497 10.1109/TSMCC.2012.2226444 10.1088/1741-2552/aace8c 10.1002/hbm.23730 |
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| Keywords | Motor Imagery (MI) Adaptive spatial–temporal graph convolutional network Brain Computer Interface(BCI) Coarse-to-fine classification approach Different joints of the same limb |
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| References | Srivastava, Hinton, Krizhevsky (b0230) 2014; 15 Li, Zhang, He (b0125) 2018; 10 Frisoli, Loconsole, Leonardis (b0030) 2012; 42 Hao, Zhang, Ma (b0115) 2016; 10 Hou Y, Jia S, Lun X, et al. Deep feature mining via attention-based BiLSTM-GCN for human motor imagery recognition[J]. arXiv preprint arXiv:2005.00777, 2020. Pfurtscheller, Neuper (b0025) 2001; 89 Delorme, Makeig (b0200) 2004; 134 Hsu, Lin, Chou (b0080) 2017; 19 Yong, Menon (b0090) 2015; 10 Chu, Zhao, Zou (b0110) 2020; 17 Supratak, Dong, Wu (b0135) 2017; 25 Yahya-Zoubir B, Bentlemsan M, Zemouri E T T, et al. Adaptive time window for EEG-based motor imagery classification[C]//Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication. 2015: 1-6. Wang Y, Liu X, Zhang Y, et al. Driving fatigue detection based on EEG signal[C]//2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). IEEE, 2015: 715-718. Gómez-Herrero, De Clercq, Anwar (b0205) 2006; 2006 Zhu, Xu, Yu (b0215) 2021, 2021 K. Choi A. Cichocki Control of a wheelchair by motor imagery in real time[C]//Intelligent Data Engineering and Automated Learning–IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings 9. Springer Berlin Heidelberg, 2008: 330-337. S. Yan, Y. Xiong, D. Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1). Sun, Zhang, Wu (b0185) 2021; 28 Vučković, Sepulveda (b0155) 2012; 34 Zhang, Yong, Menon (b0075) 2017; 12 Schirrmeister, Springenberg, Fiederer (b0145) 2017; 38 Shih, Krusienski, Wolpaw (b0005) 2012; 87 Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. Robinson, Vinod, Ang (b0050) 2013; 60 Ma, Qiu, He (b0095) 2020; 7 Hwang, Hong, Son (b0065) 2020; 23 Jia, Lin, Wang (b0210) 2021 Lawhern, Solon, Waytowich (b0190) 2018; 15 Jia, Lin, Wang (b0195) 2020 Zheng, Lu (b0120) 2015; 7 Muller-Putz, Pfurtscheller (b0015) 2007; 55 Kwak Y, Song W J, Kim S E. Graph neural network with multilevel feature fusion for EEG based brain-computer interface[C]//2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2020: 1-3. Dunsky, Dickstein (b0045) 2018; 10 Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International conference on machine learning. pmlr, 2015: 448-456. Polich (b0020) 2007; 118 Ang, Chua, Phua (b0040) 2015; 46 Lu, Li, Ren (b0150) 2016; 25 Teo, Chew (b0010) 2014; 6 Thodoroff, Pineau, Lim (b0140) 2016 Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014. Tavakolan, Frehlick, Yong (b0070) 2017; 12 Zheng, Zhu, Qin (b0085) 2018; 275 Ofner, Schwarz, Pereira (b0105) 2017; 12 Ma, Qiu, He (b0165) 2022; 30 Ma, Qiu, Wei (b0160) 2019; 28 Herman, Prasad, McGinnity (b0060) 2008; 16 Van der Maaten, Hinton (b0240) 2008; 9 Vuckovic, Sepulveda (b0100) 2008; 46 Ma (10.1016/j.bspc.2023.105885_b0160) 2019; 28 Sun (10.1016/j.bspc.2023.105885_b0185) 2021; 28 10.1016/j.bspc.2023.105885_b0225 Muller-Putz (10.1016/j.bspc.2023.105885_b0015) 2007; 55 Yong (10.1016/j.bspc.2023.105885_b0090) 2015; 10 Pfurtscheller (10.1016/j.bspc.2023.105885_b0025) 2001; 89 Thodoroff (10.1016/j.bspc.2023.105885_b0140) 2016 Vučković (10.1016/j.bspc.2023.105885_b0155) 2012; 34 Lawhern (10.1016/j.bspc.2023.105885_b0190) 2018; 15 10.1016/j.bspc.2023.105885_b0220 Supratak (10.1016/j.bspc.2023.105885_b0135) 2017; 25 10.1016/j.bspc.2023.105885_b0180 Zhu (10.1016/j.bspc.2023.105885_b0215) 2021 Srivastava (10.1016/j.bspc.2023.105885_b0230) 2014; 15 Hao (10.1016/j.bspc.2023.105885_b0115) 2016; 10 Gómez-Herrero (10.1016/j.bspc.2023.105885_b0205) 2006; 2006 Lu (10.1016/j.bspc.2023.105885_b0150) 2016; 25 Herman (10.1016/j.bspc.2023.105885_b0060) 2008; 16 Dunsky (10.1016/j.bspc.2023.105885_b0045) 2018; 10 Zheng (10.1016/j.bspc.2023.105885_b0085) 2018; 275 Chu (10.1016/j.bspc.2023.105885_b0110) 2020; 17 Shih (10.1016/j.bspc.2023.105885_b0005) 2012; 87 Polich (10.1016/j.bspc.2023.105885_b0020) 2007; 118 Ang (10.1016/j.bspc.2023.105885_b0040) 2015; 46 Ma (10.1016/j.bspc.2023.105885_b0095) 2020; 7 Zhang (10.1016/j.bspc.2023.105885_b0075) 2017; 12 Delorme (10.1016/j.bspc.2023.105885_b0200) 2004; 134 10.1016/j.bspc.2023.105885_b0035 Zheng (10.1016/j.bspc.2023.105885_b0120) 2015; 7 Robinson (10.1016/j.bspc.2023.105885_b0050) 2013; 60 10.1016/j.bspc.2023.105885_b0235 Li (10.1016/j.bspc.2023.105885_b0125) 2018; 10 10.1016/j.bspc.2023.105885_b0130 Hsu (10.1016/j.bspc.2023.105885_b0080) 2017; 19 Ma (10.1016/j.bspc.2023.105885_b0165) 2022; 30 10.1016/j.bspc.2023.105885_b0175 Frisoli (10.1016/j.bspc.2023.105885_b0030) 2012; 42 10.1016/j.bspc.2023.105885_b0055 10.1016/j.bspc.2023.105885_b0170 Jia (10.1016/j.bspc.2023.105885_b0210) 2021 Jia (10.1016/j.bspc.2023.105885_b0195) 2020 Van der Maaten (10.1016/j.bspc.2023.105885_b0240) 2008; 9 Vuckovic (10.1016/j.bspc.2023.105885_b0100) 2008; 46 Ofner (10.1016/j.bspc.2023.105885_b0105) 2017; 12 Tavakolan (10.1016/j.bspc.2023.105885_b0070) 2017; 12 Schirrmeister (10.1016/j.bspc.2023.105885_b0145) 2017; 38 Teo (10.1016/j.bspc.2023.105885_b0010) 2014; 6 Hwang (10.1016/j.bspc.2023.105885_b0065) 2020; 23 |
| References_xml | – volume: 46 start-page: 310 year: 2015 end-page: 320 ident: b0040 article-title: A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke publication-title: Clin. EEG Neurosci. – volume: 15 year: 2018 ident: b0190 article-title: EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces publication-title: J. Neural Eng. – reference: S. Yan, Y. Xiong, D. Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1). – volume: 118 start-page: 2128 year: 2007 end-page: 2148 ident: b0020 article-title: Updating P300: an integrative theory of P3a and P3b publication-title: Clin. Neurophysiol. – reference: Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014. – start-page: 1047 year: 2021 end-page: 1056 ident: b0210 article-title: HetEmotionNet: two-stream heterogeneous graph recurrent neural network for multi-modal emotion recognition[C]//Proceedings of publication-title: The 29th ACM International Conference on Multimedia – volume: 23 start-page: 1323 year: 2020 end-page: 1335 ident: b0065 article-title: Learning CNN features from DE features for EEG-based emotion recognition publication-title: Pattern Anal. Appl. – volume: 12 start-page: e0174161 year: 2017 ident: b0070 article-title: Classifying three imaginary states of the same upper extremity using time-domain features publication-title: PLoS One – volume: 30 start-page: 496 year: 2022 end-page: 508 ident: b0165 article-title: Time-distributed attention network for EEG-based motor imagery decoding from the same limb publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – reference: Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International conference on machine learning. pmlr, 2015: 448-456. – start-page: 178 year: 2016 end-page: 190 ident: b0140 article-title: Learning robust features using deep learning for automatic seizure detection publication-title: Machine learning for healthcare conference PMLR – volume: 28 start-page: 297 year: 2019 end-page: 306 ident: b0160 article-title: Deep channel-correlation network for motor imagery decoding from the same limb publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 7 start-page: 191 year: 2020 ident: b0095 article-title: Multi-channel EEG recording during motor imagery of different joints from the same limb publication-title: Sci. Data – volume: 7 start-page: 162 year: 2015 end-page: 175 ident: b0120 article-title: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks publication-title: IEEE Trans. Auton. Ment. Dev. – reference: Kwak Y, Song W J, Kim S E. Graph neural network with multilevel feature fusion for EEG based brain-computer interface[C]//2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 2020: 1-3. – start-page: 1324 year: 2020 end-page: 1330 ident: b0195 article-title: GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for publication-title: Sleep Stage Classification[C]//IJCAI. 2021 – volume: 25 start-page: 566 year: 2016 end-page: 576 ident: b0150 article-title: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 25 start-page: 1998 year: 2017 end-page: 2008 ident: b0135 article-title: DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – reference: Hou Y, Jia S, Lun X, et al. Deep feature mining via attention-based BiLSTM-GCN for human motor imagery recognition[J]. arXiv preprint arXiv:2005.00777, 2020. – volume: 10 start-page: 66 year: 2018 ident: b0045 article-title: Motor Imagery Training for Gait Rehabilitation of People with Post-Stroke Hemiparesis: Practical Applications and Protocols publication-title: Glob. J. Health Sci. – volume: 17 year: 2020 ident: b0110 article-title: Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression publication-title: J. Neural Eng. – reference: Yahya-Zoubir B, Bentlemsan M, Zemouri E T T, et al. Adaptive time window for EEG-based motor imagery classification[C]//Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication. 2015: 1-6. – volume: 16 start-page: 317 year: 2008 end-page: 326 ident: b0060 article-title: Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. – volume: 28 start-page: 219 year: 2021 end-page: 223 ident: b0185 article-title: Adaptive spatiotemporal graph convolutional networks for motor imagery classification publication-title: IEEE Signal Process Lett. – volume: 2006 start-page: 130 year: 2006 end-page: 133 ident: b0205 article-title: Automatic removal of ocular artifacts in the EEG without an EOG reference channel[C]//Proceedings of the 7th Nordic signal processing symposium-NORSIG publication-title: IEEE – start-page: 2069 year: 2021, 2021, end-page: 2080 ident: b0215 article-title: Graph contrastive learning with adaptive augmentation publication-title: Proceedings of the Web Conference – volume: 87 start-page: 268 year: 2012 end-page: 279 ident: b0005 article-title: Brain-computer interfaces in medicine publication-title: Mayo clinic proceedings Elsevier – volume: 19 start-page: 566 year: 2017 end-page: 579 ident: b0080 article-title: EEG classification of imaginary lower limb stepping movements based on fuzzy support vector machine with kernel-induced membership function publication-title: Int. J. Fuzzy Syst. – volume: 275 start-page: 869 year: 2018 end-page: 880 ident: b0085 article-title: Multiclass support matrix machine for single trial EEG classification publication-title: Neurocomputing – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: b0230 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 89 start-page: 1123 year: 2001 end-page: 1134 ident: b0025 article-title: Motor imagery and direct brain-computer communication publication-title: Proc. IEEE – volume: 60 start-page: 2123 year: 2013 end-page: 2132 ident: b0050 article-title: EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm publication-title: IEEE Trans. Biomed. Eng. – volume: 12 start-page: e0182578 year: 2017 ident: b0105 article-title: Upper limb movements can be decoded from the time-domain of low-frequency EEG publication-title: PLoS One – volume: 42 start-page: 1169 year: 2012 end-page: 1179 ident: b0030 article-title: A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks publication-title: IEEE Trans. Syst. Man Cybern. Part C (applications and Reviews) – volume: 10 start-page: 368 year: 2018 end-page: 380 ident: b0125 article-title: Hierarchical convolutional neural networks for EEG-based emotion recognition publication-title: Cogn. Comput. – reference: Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. – volume: 134 start-page: 9 year: 2004 end-page: 21 ident: b0200 article-title: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis publication-title: J. Neurosci. Methods – volume: 46 start-page: 529 year: 2008 end-page: 539 ident: b0100 article-title: Delta band contribution in cue based single trial classification of real and imaginary wrist movements publication-title: Med. Biol. Eng. Compu. – volume: 10 start-page: e0121896 year: 2015 ident: b0090 article-title: EEG classification of different imaginary movements within the same limb publication-title: PLoS One – volume: 34 start-page: 964 year: 2012 end-page: 971 ident: b0155 article-title: A two-stage four-class BCI based on imaginary movements of the left and the right wrist publication-title: Med. Eng. Phys. – volume: 55 start-page: 361 year: 2007 end-page: 364 ident: b0015 article-title: Control of an electrical prosthesis with an SSVEP-based BCI publication-title: IEEE Trans. Biomed. Eng. – volume: 38 start-page: 5391 year: 2017 end-page: 5420 ident: b0145 article-title: Deep learning with convolutional neural networks for EEG decoding and visualization publication-title: Hum. Brain Mapp. – volume: 9 year: 2008 ident: b0240 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – reference: K. Choi A. Cichocki Control of a wheelchair by motor imagery in real time[C]//Intelligent Data Engineering and Automated Learning–IDEAL 2008: 9th International Conference Daejeon, South Korea, November 2-5, 2008 Proceedings 9. Springer Berlin Heidelberg, 2008: 330-337. – reference: Wang Y, Liu X, Zhang Y, et al. Driving fatigue detection based on EEG signal[C]//2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC). IEEE, 2015: 715-718. – volume: 12 start-page: e0188293 year: 2017 ident: b0075 article-title: Evaluating the versatility of EEG models generated from motor imagery tasks: An exploratory investigation on upper-limb elbow-centered motor imagery tasks publication-title: PLoS One – volume: 6 start-page: 723 year: 2014 end-page: 728 ident: b0010 article-title: Is motor-imagery brain-computer interface feasible in stroke rehabilitation? publication-title: PM&R – volume: 10 start-page: 417 year: 2016 end-page: 439 ident: b0115 article-title: Deep learning publication-title: Int. J. Seman. Comput. – volume: 28 start-page: 297 issue: 1 year: 2019 ident: 10.1016/j.bspc.2023.105885_b0160 article-title: Deep channel-correlation network for motor imagery decoding from the same limb publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2019.2953121 – volume: 25 start-page: 566 issue: 6 year: 2016 ident: 10.1016/j.bspc.2023.105885_b0150 article-title: A deep learning scheme for motor imagery classification based on restricted Boltzmann machines publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2016.2601240 – volume: 46 start-page: 529 year: 2008 ident: 10.1016/j.bspc.2023.105885_b0100 article-title: Delta band contribution in cue based single trial classification of real and imaginary wrist movements publication-title: Med. Biol. Eng. Compu. doi: 10.1007/s11517-008-0345-8 – ident: 10.1016/j.bspc.2023.105885_b0180 – volume: 60 start-page: 2123 issue: 8 year: 2013 ident: 10.1016/j.bspc.2023.105885_b0050 article-title: EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2013.2248153 – volume: 19 start-page: 566 year: 2017 ident: 10.1016/j.bspc.2023.105885_b0080 article-title: EEG classification of imaginary lower limb stepping movements based on fuzzy support vector machine with kernel-induced membership function publication-title: Int. J. Fuzzy Syst. doi: 10.1007/s40815-016-0259-9 – start-page: 1047 year: 2021 ident: 10.1016/j.bspc.2023.105885_b0210 article-title: HetEmotionNet: two-stream heterogeneous graph recurrent neural network for multi-modal emotion recognition[C]//Proceedings of – volume: 10 start-page: 66 issue: 11 year: 2018 ident: 10.1016/j.bspc.2023.105885_b0045 article-title: Motor Imagery Training for Gait Rehabilitation of People with Post-Stroke Hemiparesis: Practical Applications and Protocols publication-title: Glob. J. Health Sci. doi: 10.5539/gjhs.v10n11p66 – volume: 30 start-page: 496 year: 2022 ident: 10.1016/j.bspc.2023.105885_b0165 article-title: Time-distributed attention network for EEG-based motor imagery decoding from the same limb publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2022.3154369 – volume: 10 start-page: e0121896 issue: 4 year: 2015 ident: 10.1016/j.bspc.2023.105885_b0090 article-title: EEG classification of different imaginary movements within the same limb publication-title: PLoS One doi: 10.1371/journal.pone.0121896 – start-page: 1324 year: 2020 ident: 10.1016/j.bspc.2023.105885_b0195 article-title: GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for publication-title: Sleep Stage Classification[C]//IJCAI. 2021 – volume: 7 start-page: 191 issue: 1 year: 2020 ident: 10.1016/j.bspc.2023.105885_b0095 article-title: Multi-channel EEG recording during motor imagery of different joints from the same limb publication-title: Sci. Data doi: 10.1038/s41597-020-0535-2 – volume: 87 start-page: 268 issue: 3 year: 2012 ident: 10.1016/j.bspc.2023.105885_b0005 article-title: Brain-computer interfaces in medicine publication-title: Mayo clinic proceedings Elsevier doi: 10.1016/j.mayocp.2011.12.008 – volume: 55 start-page: 361 issue: 1 year: 2007 ident: 10.1016/j.bspc.2023.105885_b0015 article-title: Control of an electrical prosthesis with an SSVEP-based BCI publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2007.897815 – start-page: 178 year: 2016 ident: 10.1016/j.bspc.2023.105885_b0140 article-title: Learning robust features using deep learning for automatic seizure detection publication-title: Machine learning for healthcare conference PMLR – volume: 10 start-page: 368 year: 2018 ident: 10.1016/j.bspc.2023.105885_b0125 article-title: Hierarchical convolutional neural networks for EEG-based emotion recognition publication-title: Cogn. Comput. doi: 10.1007/s12559-017-9533-x – ident: 10.1016/j.bspc.2023.105885_b0175 doi: 10.1109/ICCE-Asia49877.2020.9276983 – ident: 10.1016/j.bspc.2023.105885_b0220 doi: 10.1609/aaai.v32i1.12328 – volume: 275 start-page: 869 year: 2018 ident: 10.1016/j.bspc.2023.105885_b0085 article-title: Multiclass support matrix machine for single trial EEG classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.09.030 – volume: 10 start-page: 417 issue: 03 year: 2016 ident: 10.1016/j.bspc.2023.105885_b0115 article-title: Deep learning publication-title: Int. J. Seman. Comput. doi: 10.1142/S1793351X16500045 – volume: 34 start-page: 964 issue: 7 year: 2012 ident: 10.1016/j.bspc.2023.105885_b0155 article-title: A two-stage four-class BCI based on imaginary movements of the left and the right wrist publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2011.11.001 – volume: 16 start-page: 317 issue: 4 year: 2008 ident: 10.1016/j.bspc.2023.105885_b0060 article-title: Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2008.926694 – ident: 10.1016/j.bspc.2023.105885_b0035 doi: 10.1007/978-3-540-88906-9_42 – ident: 10.1016/j.bspc.2023.105885_b0130 doi: 10.1109/IMCCC.2015.156 – volume: 12 start-page: e0182578 issue: 8 year: 2017 ident: 10.1016/j.bspc.2023.105885_b0105 article-title: Upper limb movements can be decoded from the time-domain of low-frequency EEG publication-title: PLoS One doi: 10.1371/journal.pone.0182578 – volume: 12 start-page: e0188293 issue: 11 year: 2017 ident: 10.1016/j.bspc.2023.105885_b0075 article-title: Evaluating the versatility of EEG models generated from motor imagery tasks: An exploratory investigation on upper-limb elbow-centered motor imagery tasks publication-title: PLoS One doi: 10.1371/journal.pone.0188293 – volume: 15 start-page: 1929 issue: 1 year: 2014 ident: 10.1016/j.bspc.2023.105885_b0230 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 118 start-page: 2128 issue: 10 year: 2007 ident: 10.1016/j.bspc.2023.105885_b0020 article-title: Updating P300: an integrative theory of P3a and P3b publication-title: Clin. Neurophysiol. doi: 10.1016/j.clinph.2007.04.019 – volume: 28 start-page: 219 year: 2021 ident: 10.1016/j.bspc.2023.105885_b0185 article-title: Adaptive spatiotemporal graph convolutional networks for motor imagery classification publication-title: IEEE Signal Process Lett. doi: 10.1109/LSP.2021.3049683 – volume: 134 start-page: 9 issue: 1 year: 2004 ident: 10.1016/j.bspc.2023.105885_b0200 article-title: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2003.10.009 – volume: 25 start-page: 1998 issue: 11 year: 2017 ident: 10.1016/j.bspc.2023.105885_b0135 article-title: DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2017.2721116 – volume: 6 start-page: 723 issue: 8 year: 2014 ident: 10.1016/j.bspc.2023.105885_b0010 article-title: Is motor-imagery brain-computer interface feasible in stroke rehabilitation? publication-title: PM&R doi: 10.1016/j.pmrj.2014.01.006 – volume: 17 issue: 4 year: 2020 ident: 10.1016/j.bspc.2023.105885_b0110 article-title: Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aba7cd – ident: 10.1016/j.bspc.2023.105885_b0225 – start-page: 2069 year: 2021 ident: 10.1016/j.bspc.2023.105885_b0215 article-title: Graph contrastive learning with adaptive augmentation publication-title: Proceedings of the Web Conference – ident: 10.1016/j.bspc.2023.105885_b0170 – volume: 46 start-page: 310 issue: 4 year: 2015 ident: 10.1016/j.bspc.2023.105885_b0040 article-title: A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke publication-title: Clin. EEG Neurosci. doi: 10.1177/1550059414522229 – ident: 10.1016/j.bspc.2023.105885_b0055 doi: 10.1145/2816839.2816845 – volume: 12 start-page: e0174161 issue: 3 year: 2017 ident: 10.1016/j.bspc.2023.105885_b0070 article-title: Classifying three imaginary states of the same upper extremity using time-domain features publication-title: PLoS One doi: 10.1371/journal.pone.0174161 – volume: 89 start-page: 1123 issue: 7 year: 2001 ident: 10.1016/j.bspc.2023.105885_b0025 article-title: Motor imagery and direct brain-computer communication publication-title: Proc. IEEE doi: 10.1109/5.939829 – volume: 9 issue: 11 year: 2008 ident: 10.1016/j.bspc.2023.105885_b0240 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 23 start-page: 1323 year: 2020 ident: 10.1016/j.bspc.2023.105885_b0065 article-title: Learning CNN features from DE features for EEG-based emotion recognition publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-019-00860-w – volume: 7 start-page: 162 issue: 3 year: 2015 ident: 10.1016/j.bspc.2023.105885_b0120 article-title: Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks publication-title: IEEE Trans. Auton. Ment. Dev. doi: 10.1109/TAMD.2015.2431497 – volume: 42 start-page: 1169 issue: 6 year: 2012 ident: 10.1016/j.bspc.2023.105885_b0030 article-title: A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks publication-title: IEEE Trans. Syst. Man Cybern. Part C (applications and Reviews) doi: 10.1109/TSMCC.2012.2226444 – volume: 2006 start-page: 130 year: 2006 ident: 10.1016/j.bspc.2023.105885_b0205 article-title: Automatic removal of ocular artifacts in the EEG without an EOG reference channel[C]//Proceedings of the 7th Nordic signal processing symposium-NORSIG publication-title: IEEE – volume: 15 issue: 5 year: 2018 ident: 10.1016/j.bspc.2023.105885_b0190 article-title: EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aace8c – volume: 38 start-page: 5391 issue: 11 year: 2017 ident: 10.1016/j.bspc.2023.105885_b0145 article-title: Deep learning with convolutional neural networks for EEG decoding and visualization publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23730 – ident: 10.1016/j.bspc.2023.105885_b0235 |
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