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BNCI 2014-001 Motor Imagery dataset

nm000139 · 89 high-confidence citations

1. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

   Vernon J Lawhern, Amelia J Solon, Nicholas R Waytowich, Stephen M Gordon, Chou P Hung, Brent J Lance · 2018 · Journal of Neural Engineering

   Cites data paper 4,226 citations

2. Deep learning with convolutional neural networks for EEG decoding and visualization

   Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas D. J. Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball · 2017 · Human Brain Mapping

   Cites data paper 3,473 citations

3. Deep learning for electroencephalogram (EEG) classification tasks: a review

   Alexander Craik, Yongtian He, José L. Contreras-Vidal · 2019 · Journal of Neural Engineering

   Cites data paper 1,641 citations

4. Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

   Kai Keng Ang, Zheng Yang Chin, Chuanchu Wang, Cuntai Guan, Haihong Zhang · 2012 · Frontiers in Neuroscience

   Cites data paper 1,225 citations

5. Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data

   Tijl Grootswagers, Susan G. Wardle, Thomas A. Carlson · 2016 · Journal of Cognitive Neuroscience

   Cites data paper 825 citations

6. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

   Siavash Sakhavi, Cuntai Guan, Shuicheng Yan · 2018 · IEEE Transactions on Neural Networks and Learning Systems

   Cites data paper 657 citations

7. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

   Min-Ho Lee, Oyeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong–Whan Lee · 2019 · GigaScience

   Cites data paper 564 citations

8. A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces

   Yijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao · 2016 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

   Cites data paper 492 citations

9. Transfer Learning in Brain-Computer Interfaces Abstract\uFFFDThe performance of brain-computer interfaces (BCIs) improves with the amount of avail

   Vinay Jayaram, Morteza Alamgir, Yasemin Altün, Bernhard Schölkopf, Moritz Grosse‐Wentrup · 2016 · IEEE Computational Intelligence Magazine

   Cites data paper 432 citations

10. Deep learning for motor imagery EEG-based classification: A review

    Ali Al-Saegh, Shefa A. Dawwd, Jassim M. Abdul-Jabbar · 2020 · Biomedical Signal Processing and Control

    Cites data paper 431 citations

11. Robust artifactual independent component classification for BCI practitioners

    Irene Winkler, Stephanie Brandl, Franziska Horn, Eric Waldburger, Carsten Allefeld, Michael Tangermann · 2014 · Journal of Neural Engineering

    Cites data paper 338 citations

12. Correlation-based channel selection and regularized feature optimization for MI-based BCI

    Jing Jin, Yangyang Miao, Ian Daly, Cili Zuo, Dewen Hu, Andrzej Cichocki · 2019 · Neural Networks

    Cites data paper 335 citations

13. EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

    Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, Luca Benini · 2020 · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)

    Cites data paper 310 citations

14. A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification

    Xinqiao Zhao, Hongmiao Zhang, Guilin Zhu, Fengxiang You, Shaolong Kuang, Lining Sun · 2019 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 303 citations

15. Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network

    Kaishuo Zhang, Neethu Robinson, Seong‐Whan Lee, Cuntai Guan · 2020 · Neural Networks

    Cites data paper 281 citations

16. EEG-based brain–computer interfaces

    Dennis J. McFarland, Jonathan R. Wolpaw · 2017 · Current Opinion in Biomedical Engineering

    Cites data paper 269 citations

17. Open Access Dataset for EEG+NIRS Single-Trial Classification

    Jaeyoung Shin, Alexander von Lühmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han‐Jeong Hwang, Klaus‐Robert Müller · 2016 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 268 citations

18. A Novel EEG Feature Extraction Method Using Hjorth Parameter

    Seung-Hyeon Oh, Yu‐Ri Lee, Hyoung-Nam Kim · 2014 · International Journal of Electronics and Electrical Engineering

    Cites data paper 264 citations

19. Brain-Computer Interface: Advancement and Challenges

    M. F. Mridha, Sujoy Chandra Das, Md. Mohsin Kabir, Aklima Akter Lima, Md. Rashedul Islam, Yutaka Watanobe · 2021 · Sensors

    Cites data paper 255 citations

20. Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification

    He Zhao, Qingqing Zheng, Kai Ma, Huiqi Li, Yefeng Zheng · 2020 · IEEE Transactions on Neural Networks and Learning Systems

    Cites data paper 235 citations

21. Stationary common spatial patterns for brain–computer interfacing

    Wojciech Samek, Carmen Vidaurre, Klaus‐Robert Müller, Motoaki Kawanabe · 2012 · Journal of Neural Engineering

    Cites data paper 212 citations

22. Riemannian Procrustes Analysis: Transfer Learning for Brain–Computer Interfaces

    Pedro Luiz Coelho Rodrigues, Christian Jutten, Marco Congedo · 2018 · IEEE Transactions on Biomedical Engineering

    Cites data paper 210 citations

23. Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

    Jaeyoung Shin, Alexander von Lühmann, Do-Won Kim, Jan Mehnert, Han-Jeong Hwang, Klaus‐Robert Müller · 2018 · Scientific Data

    Cites data paper 207 citations

24. EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

    Ce Zhang, Young‐Keun Kim, Azim Eskandarian · 2021 · Journal of Neural Engineering

    Cites data paper 202 citations

25. A novel hybrid deep learning scheme for four-class motor imagery classification

    Ruilong Zhang, Qun Zong, Liqian Dou, Xinyi Zhao · 2019 · Journal of Neural Engineering

    Cites data paper 181 citations

26. A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals

    Zahra Khademi, Farideh Ebrahimi, Hussain Montazery Kordy · 2022 · Computers in Biology and Medicine

    Cites data paper 176 citations

27. MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

    Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn · 2021 · IEEE Transactions on Biomedical Engineering

    Cites data paper 175 citations

28. MOABB: trustworthy algorithm benchmarking for BCIs

    Vinay Jayaram, Alexandre Barachant · 2018 · Journal of Neural Engineering

    Cites data paper 173 citations

29. An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

    Arunabha M. Roy · 2022 · Biomedical Signal Processing and Control

    Cites data paper 169 citations

30. Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems

    Amirhossein S. Aghaei, Mohammad Shahin Mahanta, Konstantinos N. Plataniotis · 2015 · IEEE Transactions on Biomedical Engineering

    Cites data paper 167 citations

31. Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface

    Arunabha M. Roy · 2022 · Engineering Applications of Artificial Intelligence

    Cites data paper 166 citations

32. EEG-based workload estimation across affective contexts

    Christian MÃ ⁄ hl, Camille Jeunet, Fabien Lotte · 2014 · Frontiers in Neuroscience

    Cites data paper 165 citations

33. Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks

    Ji-Seon Bang, Min-Ho Lee, Siamac Fazli, Cuntai Guan, Seong–Whan Lee · 2021 · IEEE Transactions on Neural Networks and Learning Systems

    Cites data paper 165 citations

34. A Temporal-Spectral-Based Squeeze-and- Excitation Feature Fusion Network for Motor Imagery EEG Decoding

    Yang Li, Lianghui Guo, Yu Liu, Jingyu Liu, Fangang Meng · 2021 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 164 citations

35. GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals

    Yimin Hou, Shuyue Jia, Xiangmin Lun, Ziqian Hao, Yan Shi, Yang Li, Rui Zeng, Jinglei Lv · 2022 · IEEE Transactions on Neural Networks and Learning Systems

    Cites data paper 164 citations

36. Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

    Tian-jian Luo, Changle Zhou, Fei Chao · 2018 · BMC Bioinformatics

    Cites data paper 161 citations

37. Common Bayesian Network for Classification of EEG-Based Multiclass Motor Imagery BCI

    Lianghua He, Die Hu, Meng Wan, Ying Wen, Karen M. von Deneen, MengChu Zhou · 2015 · IEEE Transactions on Systems Man and Cybernetics Systems

    Cites data paper 157 citations

38. Brain–Computer Interface for Neurorehabilitation of Upper Limb After Stroke

    Kai Keng Ang, Cuntai Guan · 2015 · Proceedings of the IEEE

    Cites data paper 146 citations

39. P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier

    Maarten De Vos, Markus Kroesen, Reiner Emkes, Stefan Debener · 2014 · Journal of Neural Engineering

    Cites data paper 146 citations

40. A CSP\AM-BA-SVM Approach for Motor Imagery BCI System

    Sahar Selim, Manal Tantawi, Howida A. Shedeed, Amr Badr · 2018 · IEEE Access

    Cites data paper 145 citations

41. Classification of multiple motor imagery using deep convolutional neural networks and spatial filters

    Brenda Elizabeth Olivas-Padilla, Mario I. Chacón-Murguía · 2018 · Applied Soft Computing

    Cites data paper 141 citations

42. Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination

    Davide Borra, Silvia Fantozzi, Elisa Magosso · 2020 · Neural Networks

    Cites data paper 140 citations

43. Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

    Jing Jin, Chang Liu, Ian Daly, Yangyang Miao, Shurui Li, Xingyu Wang, Andrzej Cichocki · 2020 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 139 citations

44. A review of critical challenges in MI-BCI: From conventional to deep learning methods

    Zahra Khademi, Farideh Ebrahimi, Hussain Montazery Kordy · 2022 · Journal of Neuroscience Methods

    Cites data paper 131 citations

45. An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using Granger causality

    Hesam Varsehi, Mohammad Firoozabadi · 2020 · Neural Networks

    Cites data paper 131 citations

46. Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals

    Nitesh Singh Malan, Shiru Sharma · 2019 · Computers in Biology and Medicine

    Cites data paper 128 citations

47. FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface

    Ravikiran Mane, Effie Chew, Karen Sui Geok Chua, Kai Keng Ang, Neethu Robinson, A. P. Vinod, Seong–Whan Lee, Cuntai Guan · 2021 · arXiv (Cornell University)

    Cites data paper 126 citations

48. Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification

    Yangyang Miao, Jing Jin, Ian Daly, Cili Zuo, Xingyu Wang, Andrzej Cichocki, Tzyy‐Ping Jung · 2021 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 125 citations

49. Cross-Dataset Variability Problem in EEG Decoding With Deep Learning

    Lichao Xu, Minpeng Xu, Yufeng Ke, Xingwei An, Shuang Liu, Dong Ming · 2020 · Frontiers in Human Neuroscience

    Cites data paper 123 citations

50. Removal of EOG Artifacts from Single Channel EEG Signals using Combined Singular Spectrum Analysis and Adaptive Noise Canceler

    Ajay Kumar Maddirala, Rafi Ahamed Shaik · 2016 · IEEE Sensors Journal

    Cites data paper 122 citations

51. Transfer Learning in Brain-Computer Interfaces

    Vinay Jayaram, Morteza Alamgir, Yasemin Altün, Bernhard Schölkopf, Moritz Grosse‐Wentrup · 2015 · arXiv (Cornell University)

    Cites data paper 119 citations

52. Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

    Haider Raza, Hubert Cecotti, Yuhua Li, Girijesh Prasad · 2015 · Soft Computing

    Cites data paper 116 citations

53. FBMSNet: A Filter-Bank Multi-Scale Convolutional Neural Network for EEG-Based Motor Imagery Decoding

    Ke Liu, Mingzhao Yang, Zhuliang Yu, Guoyin Wang, Wei Wu · 2022 · IEEE Transactions on Biomedical Engineering

    Cites data paper 115 citations

54. An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing

    Xiaying Wang, Michael Hersche, Batuhan Tömekçe, Burak Kaya, Michele Magno, Luca Benini · 2020 · n/a

    Cites data paper 114 citations

55. Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition

    Nicolás Nieto, Victoria Peterson, Hugo Leonardo Rufiner, Juan E. Kamienkowski, Rubén D. Spies · 2022 · Scientific Data

    Cites data paper 112 citations

56. IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG

    Jiaheng Wang, Lin Yao, Yueming Wang · 2023 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 112 citations

57. On the Vulnerability of CNN Classifiers in EEG-Based BCIs

    Xiao Zhang, Dongrui Wu · 2019 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 112 citations

58. A Self-Adaptive Online Brain–Machine Interface of a Humanoid Robot Through a General Type-2 Fuzzy Inference System

    Javier Andreu-Pérez, Fan Cao, Hani Hagras, Guang‐Zhong Yang · 2016 · IEEE Transactions on Fuzzy Systems

    Cites data paper 109 citations

59. Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface

    Haider Raza, Dheeraj Rathee, Shang‐Ming Zhou, Hubert Cecotti, Girijesh Prasad · 2019 · Neurocomputing

    Cites data paper 108 citations

60. Separated channel convolutional neural network to realize the training free motor imagery BCI systems

    Xuyang Zhu, Peiyang Li, Cunbo Li, Dezhong Yao, Rui Zhang, Peng Xu · 2018 · Biomedical Signal Processing and Control

    Cites data paper 106 citations

61. Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

    Ciaran Cooney, Attila Korik, Raffaella Folli, Damien Coyle · 2020 · Sensors

    Cites data paper 106 citations

62. Online SSVEP-based BCI using Riemannian geometry

    Emmanuel Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Éric Monacelli, Yskandar Hamam · 2016 · Neurocomputing

    Cites data paper 104 citations

63. PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task

    Sanjay Kumar, H. Hannah Inbarani · 2016 · Neural Computing and Applications

    Cites data paper 103 citations

64. How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art

    Pasquale Arpaïa, Antonio Espósito, Angela Natalizio, Marco Parvis · 2022 · Journal of Neural Engineering

    Cites data paper 100 citations

65. MI-EEGNET: A novel convolutional neural network for motor imagery classification

    Mouad Riyad, Mohammed Khalil, Abdellah Adib · 2020 · Journal of Neuroscience Methods

    Cites data paper 100 citations

66. Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification

    Seung-Bo Lee, Hyun-Ji Kim, Hakseung Kim, Ji-Hoon Jeong, Seong‐Whan Lee, Dong‐Joo Kim · 2019 · Information Sciences

    Cites data paper 100 citations

67. Human motor decoding from neural signals: a review

    Wing-kin Tam, Tong Wu, Qi Zhao, Edward W. Keefer, Zhi Yang · 2019 · BMC Biomedical Engineering

    Cites data paper 97 citations

68. Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability

    Anirban Chowdhury, Yogesh Kumar Meena, Haider Raza, Braj Bhushan, Ashwani Kumar Uttam, Nirmal Pandey, Adnan Ariz Hashmi, Alok Bajpai, Ashish Dutta, Girijesh Prasad · 2018 · IEEE Journal of Biomedical and Health Informatics

    Cites data paper 96 citations

69. Data augmentation for self-paced motor imagery classification with C-LSTM

    Daniel Freer, Guang‐Zhong Yang · 2019 · Journal of Neural Engineering

    Cites data paper 95 citations

70. Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

    Xia Chen, Xiangbin Teng, Han Chen, Yafeng Pan, Philipp Geyer · 2023 · Biomedical Signal Processing and Control

    Cites data paper 95 citations

71. Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

    Ikhtiyor Majidov, Taeg Keun Whangbo · 2019 · Sensors

    Cites data paper 95 citations

72. Feature extraction of four-class motor imagery EEG signals based on functional brain network

    Qingsong Ai, Anqi Chen, Kun Chen, Quan Liu, Tichao Zhou, Sijin Xin, Ze Ji · 2019 · Journal of Neural Engineering

    Cites data paper 91 citations

73. Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification

    Ce Ju, Cuntai Guan · 2022 · IEEE Transactions on Neural Networks and Learning Systems

    Cites data paper 90 citations

74. Thinker invariance: enabling deep neural networks for BCI across more people

    Demetres Kostas, Frank Rudzicz · 2020 · Journal of Neural Engineering

    Cites data paper 90 citations

75. EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface

    Mahnaz Arvaneh, Cuntai Guan, Kai Keng Ang, Chai Quek · 2013 · Neural Computation

    Cites data paper 88 citations

76. Dynamic frequency feature selection based approach for classification of motor imageries

    Jing Luo, Zuren Feng, Jun Zhang, Na Lu · 2016 · Computers in Biology and Medicine

    Cites data paper 88 citations

77. Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

    Johannes Höhne, Elisa Mira Holz, Pit Staiger-Sälzer, Klaus‐Robert Müller, Andrea Kübler, Michael Tangermann · 2014 · PLoS ONE

    Cites data paper 87 citations

78. SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding

    Chang Liu, Jing Jin, Ian Daly, Shurui Li, Hao Sun, Yitao Huang, Xingyu Wang, Andrzej Cichocki · 2022 · IEEE Transactions on Neural Systems and Rehabilitation Engineering

    Cites data paper 87 citations

79. EEGANet: Removal of Ocular Artifacts From the EEG Signal Using Generative Adversarial Networks

    Phattarapong Sawangjai, Manatsanan Trakulruangroj, Chiraphat Boonnag, Maytus Piriyajitakonkij, Rajesh Kumar Tripathy, Thapanun Sudhawiyangkul, Theerawit Wilaiprasitporn · 2021 · IEEE Journal of Biomedical and Health Informatics

    Cites data paper 87 citations

80. Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach

    Minmin Miao, Hong Zeng, Aimin Wang, Changsen Zhao, Feixiang Liu · 2016 · Journal of Neuroscience Methods

    Cites data paper 86 citations

81. Motor imagery EEG classification based on ensemble support vector learning

    Jing Luo, Xing Gao, Xiaobei Zhu, Bin Wang, Na Lu, Jie Wang · 2020 · Computer Methods and Programs in Biomedicine

    Cites data paper 86 citations

82. EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss

    Tian-jian Luo, Ya-chao Fan, Lifei Chen, Gongde Guo, Changle Zhou · 2020 · Frontiers in Neuroinformatics

    Cites data paper 83 citations

83. Effect of mindfulness meditation on brain–computer interface performance

    Lee-Fan Tan, Zoltán Dienes, Ashok Jansari, Sing-Yau Goh · 2013 · Consciousness and Cognition

    Cites data paper 81 citations

84. A shallow mirror transformer for subject-independent motor imagery BCI

    Jing Luo, Yaojie Wang, Shuxiang Xia, Na Lu, Xiaoyong Ren, Zhenghao Shi, Xinhong Hei · 2023 · Computers in Biology and Medicine

    Cites data paper 81 citations

85. Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain–computer interfaces

    Luis F. Nicolás-Alonso, Rebeca Corralejo, Javier Gómez‐Pilar, Daniel Álvarez, Roberto Hornero · 2015 · Neurocomputing

    Cites data paper 81 citations

86. A Deep Learning Framework for Decoding Motor Imagery Tasks of the Same Hand Using EEG Signals

    Rami Alazrai, Motaz Abuhijleh, Hisham Alwanni, Mohammad I. Daoud · 2019 · IEEE Access

    Cites data paper 81 citations

87. Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

    Aiming Liu, Kun Chen, Quan Liu, Qingsong Ai, Yi Xie, Anqi Chen · 2017 · Sensors

    Cites data paper 81 citations

88. EEG Motor Imagery Classification With Sparse Spectrotemporal Decomposition and Deep Learning

    Biao Sun, Xing Zhao, Han Zhang, Ruifeng Bai, Ting Li · 2020 · IEEE Transactions on Automation Science and Engineering

    Cites data paper 79 citations

89. Parallel Spatial–Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI

    Xiuling Liu, Yonglong Shen, Jing Liu, Jianli Yang, Peng Xiong, Feng Lin · 2020 · Frontiers in Neuroscience

    Cites data paper 79 citations

No citations of that kind for this dataset.