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A list of papers for motor imagery using machine learning/deep learning.

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Motor Imagery Papers

This repo contains a list of papers for motor imagery using machine learning/deep learning.

If you have any suggested papers, please contact me ziyujia{at}bjtu.edu.cn

Contents

Paper List

Title Author Date Publication Paper Link Dataset Model
MMCNN: A Multi-branch Multi-scale Convolutional Neural Network for Motor Imagery Classification Ziyu Jia, et al. Feb-2021 ECML PKDD URL BCIC IV 2a BCIC IV 2b CNN
Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network Tang, X., Li, W., Li, X., Ma, W., & Dang, X. Jul-2020 Expert Systems with Applications URL BCIC IV 2b Private CNN
Making sense of spatio-temporal preserving representations for EEG-based human intention recognition Zhang D, Yao L, Chen K, et al. Jul-2020 IEEE transactions on cybernetics URL PhysioNet EEG Dataset CNN, LSTM
A deep CNN approach to decode motor preparation of upper limbs from time–frequency maps of EEG signals at source level Mammone, N., Ieracitano, C., & Morabito, F. C. Apr-2020 Neural Networks URL ULM CNN (CWT, TF)
Convolutional neural network based features for motor imagery EEG signals classification in brain–computer interface system Taheri, S., Ezoji, M., & Sakhaei, S. M. Mar-2020 SN Applied Sciences URL BCIC III 4a SVM
A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning Li, F., He, F., Wang, F., Zhang, D., Xia, Y., & Li, X. Feb-2020 Applied Sciences URL BCIC IV 2b SCNN (CWT)
HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification Dai, G., Zhou, J., Huang, J., & Wang, N. Jan-2020 Journal of neural engineering URL BCIC IV 2a BCIC IV 2b CNN
Application of continuous wavelet transform and convolutional neural network in decoding motor imagery brain-computer interface Lee H K, Choi Y S. Dec-2019 Entropy URL BCIC IV 2b BCIC II 3 CNN (CWT)
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion Amin S U, Alsulaiman M, Muhammad G, et al. Dec-2019 Future Generation computer systems URL BCIC IV 2a HGD CNN
A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals. Hassanpour A, Moradikia M, Adeli H, et al. Dec-2019 Expert Systems URL BCIC IV 2a GDL
Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb Ma, X., Qiu, S., Wei, W., Wang, S., & He, H. Nov-2019 IEEE Transactions on Neural Systems and Rehabilitation Engineering URL Private CNN
A parallel multiscale filter bank convolutional neural networks for motor imagery EEG classification. Wu H, Niu Y, Li F, et al. Nov-2019 Frontiers in neuroscience URL BCIC IV 2a BCIC IV 2b HGD CNN
Subject-independent brain–computer interfaces based on deep convolutional neural networks. Kwon O Y, Lee M H, Guan C, et al. Nov-2019 IEEE transactions on neural networks and learning systems URL Private CNN (SSFR)
A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network Ortiz-Echeverri C J, Salazar-Colores S, Rodríguez-Reséndiz J, et al. Oct-2019 Sensors URL BCIC III 4a CNN (CWT)
A multi-branch 3D convolutional neural network for EEG-based motor imagery classification. Zhao X, Zhang H, Zhu G, et al. Oct-2019 IEEE Transactions on Neural Systems and Rehabilitation Engineering URL BCIC IV 2a CNN
A novel hybrid deep learning scheme for four-class motor imagery classification. Zhang R, Zong Q, Dou L, et al. Oct-2019 Journal of neural engineering URL BCIC IV 2a CNN, LSTM (FBCSP)
An advanced bispectrum features for EEG-based motor imagery classification. Sun L, Feng Z, Lu N, et al. Oct-2019 Expert Systems with Applications URL Private BCIC IV 2b SVM (VSBS)
Densely feature fusion based on convolutional neural networks for motor imagery EEG classification Li D, Wang J, Xu J, et al. Sep-2019 IEEE Access URL BCIC IV 2a CNN (DFFN)
A deep learning framework for decoding motor imagery tasks of the same hand using eeg signals Alazrai R, Abuhijleh M, Alwanni H, et al. Aug-2019 IEEE Access URL Private CNN (QTFD)
A deep transfer convolutional neural network framework for EEG signal classification. Xu G, Shen X, Chen S, et al. Jul-2019 IEEE Access URL BCIC IV 2b CNN
A channel-projection mixed-scale convolutional neural network for motor imagery EEG decoding Li Y, Zhang X R, Zhang B, et al. Jun-2019 IEEE Transactions on Neural Systems and Rehabilitation Engineering URL BCIC IV 2a HGD CNN
Learning joint space–time–frequency features for EEG decoding on small labeled data. Zhao D, Tang F, Si B, et al. Jun-2019 Neural Networks URL BCIC IV 2a BCIC IV 2b ULM CNN
Motor imagery EEG classification using capsule networks Ha K W, Jeong J W. Jun-2019 Sensors URL BCIC IV 2b CNN (STFT)
Semisupervised deep stacking network with adaptive learning rate strategy for motor imagery EEG recognition. Tang X L, Ma W C, Kong D S, et al. May-2019 Neural computation URL Private BCIC IV 2b SADSN
Efficient classification of motor imagery electroencephalography signals using deep learning methods. Majidov I, Whangbo T. Apr-2019 Sensors URL BCIC IV 2a BCIC IV 2b CNN
Separated channel convolutional neural network to realize the training free motor imagery BCI systems Zhu X, Li P, Li C, et al. Mar-2019 Biomedical Signal Processing and Control URL BCIC IV 2b Private SCNN (CSP)
A convolutional recurrent attention model for subject-independent eeg signal analysis. Zhang D, Yao L, Chen K, et al. Mar-2019 IEEE Signal Processing Letters URL BCIC IV 2a CNN, RNN (CRAM)
Classification of multiple motor imagery using deep convolutional neural networks and spatial filters Olivas-Padilla B E, Chacon-Murguia M I. Feb-2019 Applied Soft Computing URL BCIC IV 2a Private CNN (DFBCSP)
Convolutional neural network based approach towards motor imagery tasks EEG signals classification Chaudhary S, Taran S, Bajaj V, et al. Feb-2019 IEEE Sensors Journal URL BCIC III 4a CNN (STFT, CWT)
Domain adaptation with source selection for motor-imagery based BCI Jeon E, Ko W, Suk H I. Feb-2019 2019 7th International Winter Conference on Brain-Computer Interface (BCI) URL BCIC IV 2a CNN (PSD)
Validating deep neural networks for online decoding of motor imagery movements from EEG signals. Tayeb Z, Fedjaev J, Ghaboosi N, et al. Jan-2019 Sensors URL BCIC IV 2b Private LSTM, CNN
Multilevel weighted feature fusion using convolutional neural networks for EEG motor imagery classification Amin S U, Alsulaiman M, Muhammad G, et al. Jan-2019 IEEE Access URL BCIC IV 2a CNN
A novel deep learning approach with data augmentation to classify motor imagery signals. Zhang Z, Duan F, Sole-Casals J, et al. Jan-2019 IEEE Access URL BCIC II 3 Private CNN, WNN
EEG classification of motor imagery using a novel deep learning framework Dai M, Zheng D, Na R, et al. Jan-2019 Sensors URL BCIC IV 2b Private CNN (STFT)
Walking imagery evaluation in brain computer interfaces via a multi-view multi-level deep polynomial network. Lei B, Liu X, Liang S, et al. Jan-2019 IEEE transactions on neural systems and rehabilitation engineering URL Private MMDPN (CSP, PSD, WPT)
Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. Ko, Li-Wei, et al. Jan-2019 IEEE Computational Intelligence Magazine URL Private MFF
Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. Xu B, Zhang L, Song A, et al. Dec-2018 IEEE Access URL BCIC II 3 BCIC IV 2a CNN
An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Dose H, Møller J S, Iversen H K, et al. Dec-2018 Expert Systems with Applications URL PhysioNet EEG Dataset CNN
Deep fusion feature learning network for MI-EEG classification. Yang J, Yao S, Wang J. Nov-2018 IEEE Access URL Private BCIC III BCIC IV CNN, LSTM (DWT)
LSTM-based EEG classification in motor imagery tasks. Wang P, Jiang A, Liu X, et al. Oct-2018 IEEE transactions on neural systems and rehabilitation engineering URL BCIC IV 2a LSTM
EEG classification using sparse Bayesian extreme learning machine for brain–computer interface. Jin Z, Zhou G, Gao D, et al. Oct-2018 Neural Computing and Applications URL BCIC IV 2b SBELM
Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. Luo T, Chao F. Sep-2018 BMC bioinformatics URL BCIC IV 2a BCIC IV 2b LSTM, GRU (FBCSP)
A hierarchical semi-supervised extreme learning machine method for EEG recognition. She Q, Hu B, Luo Z, et al. Jul-2018 Medical & biological engineering & computing URL BCIC IV 2a HSS-ELM
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Lawhern V J, Solon A J, Waytowich N R, et al. Jul-2018 Journal of neural engineering URL BCIC IV 2a CNN
The motor imagination EEG recognition combined with convolution neural network and gated recurrent unit. Cai J, Wei C, Tang X L, et al. Jul-2018 Chinese Control Conference (CCC) URL Private BCIC IV 2b CNN, GRU
A Deep Convolutional Neural Network Based Classification Of Multi-Class Motor Imagery With Improved Generalization. Kar A, Bera S, Karri S P K, et al. Jul-2018 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) URL BCIC IV 2a CNN
Temporally constrained sparse group spatial patterns for motor imagery BCI. Zhang Y, Nam C S, Zhou G, et al. Jun-2018 IEEE transactions on cybernetics URL BCIC III 3a, BCIC IV 2a, BCIC IV 2b SVM (TSGSP)
Classification of multi-class BCI data by common spatial pattern and fuzzy system Nguyen T, Hettiarachchi I, Khatami A, et al. May-2018 IEEE Access URL BCIC III 3a, BCIC IV 2a FLS
Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces. Zhang Y, Wang Y, Zhou G, et al. Apr-2018 Expert Systems with Applications URL BCIC III 4a BCIC IV 2b MKELM
Learning temporal information for brain-computer interface using convolutional neural networks. Sakhavi S, Guan C, Yan S. Mar-2018 IEEE transactions on neural networks and learning systems URL BCIC IV 2a CNN (FBCSP)
Deep recurrent spatio-temporal neural network for motor imagery based BCI. Ko W, Yoon J, Kang E, et al. Jan-2018 2018 6th International Conference on Brain-Computer Interface (BCI) URL BCIC IV 2a CNN, RNN
Classification of motor imagery for Ear-EEG based brain-computer interface. Kim Y J, Kwak N S, Lee S W. Jan-2018 2018 6th International Conference on Brain-Computer Interface (BCI) URL Private BCIC III 4a CSP
A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image. Lee, Hyeon Kyu, and Young-Seok Choi. Jan-2018 2018 International Conference on Information Networking (ICOIN) URL BCIC IV 2b CNN (CWT)
Deep convolutional neural network for decoding motor imagery based brain computer interface. Zhang J, Yan C, Gong X. Oct-2017 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) URL Private CNN (STFT)
Deep learning with convolutional neural networks for EEG decoding and visualization. Schirrmeister R T, Springenberg J T, Fiederer L D J, et al. Aug-2017 Human brain mapping URL BCIC IV 2a HGD CNN
EEG feature extraction and classification in multiclass multiuser motor imagery brain computer interface u sing Bayesian Network and ANN. Sagee G S, Hema S. Jul-2017 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) URL PhysioNet EEG Dataset BN
A deep learning approach for motor imagery EEG signal classification. Kumar S, Sharma A, Mamun K, et al. Dec-2016 2016 3rd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) URL BCIC III 4a CSP
A novel deep learning approach for classification of EEG motor imagery signals. Tabar Y R, Halici U. Nov-2016 Journal of neural engineering URL BCIC II 3 BCIC IV 2b CNN (SAE)
A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. Lu N, Li T, Ren X, et al. Aug-2016 IEEE transactions on neural systems and rehabilitation engineering URL BCIC IV 2b RBM (FFT, WPD)
A multi-label classification method for detection of combined motor imageries. Lindig-Leon C, Bougrain L. Oct-2015 2015 IEEE International Conference on Systems, Man, and Cybernetics URL Private CSP
On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification. Yang H, Sakhavi S, Ang K K, et al. Aug-2015 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) URL BCIC IV 2a CNN (FCMS)
Parallel convolutional-linear neural network for motor imagery classification. Sakhavi S, Guan C, Yan S. Aug-2015 2015 23rd European Signal Processing Conference (EUSIPCO) URL BCIC IV 2a CNN
Increase performance of four-class classification for motor-imagery based brain-computer interface. Temiyasathit C. Jul-2014 2014 International Conference on Computer, Information and Telecommunication Systems (CITS) URL BCIC IV 2a CSP
A novel classification method for motor imagery based on Brain-Computer Interface. Chen C Y, Wu C W, Lin C T, et al. Jul-2014 2014 International Joint Conference on Neural Networks (IJCNN). URL Private LDA (CSP)
Neural network-based three-class motor imagery classification using time-domain features for BCI applications. Hamedi M, Salleh S H, Noor A M, et al. Apr-2014 2014 IEEE Region 10 Symposium URL Private MLP, RBF
EEG feature comparison and classification of simple and compound limb motor imagery. Yi W, Qiu S, Qi H, et al. Oct-2013 Journal of neuroengineering and rehabilitation URL Private CSP (SVM)
Evolving spatial and frequency selection filters for brain-computer interfaces. Aler R, Galván I M, Valls J M. Jul-2010 IEEE congress on evolutionary computation URL BCIC III CSP

Datasets

Dataset Paper Link Download
Upper limb movements(ULM) Upper limb movements can be decoded from the time-domain of low-frequency EEG Link URL
High gamma dataset(HGD) Deep learning with convolutional neural networks for EEG decoding and visualization Link URL
PhysioNet EEG Dataset PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals Link URL
BCI Competition Link

Acknowledgement

Tianhang Liu, Kaixin Yang, Ziyu Jia, and Xiyang Cai collaborated to organize and summarize the above papers.

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