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

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease

Cappello, V., Marchetti, L., Parlanti, P., Landi, S., Tonazzini, I., Cecchini, M., Piazza, V., & Gemmi, M. (2016) · Scientific Reports · doi:10.1038/s41598-016-0001-8 · Cited by 13,097 · see citing works

Show and tell: A neural image caption generator

Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · see citing works

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works

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

Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · cited 1× · see citing works

Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751 · see citing works

Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973 · see citing works

Optimal spatial filtering of single trial EEG during imagined hand movement

Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895946 · Cited by 2,457 · see citing works

A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239 · see citing works

Motor imagery and direct brain-computer communication

Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990 · see citing works

MNE software for processing MEG and EEG data

Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C. J., Parkkonen, L. T., & Hämäläinen, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.027 · Cited by 1,924 · see citing works

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

Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758 · see citing works

Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547 · see citing works

Deep learning-based electroencephalography analysis: a systematic review

Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532 · see citing works

Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface

Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008) · 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) · doi:10.1109/ijcnn.2008.4634130 · Cited by 1,514 · see citing works

Review of the BCI Competition IV

Tangermann, M., Müller, K.-R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K. J., Müller-Putz, G. R., et al. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00055 · Cited by 1,288 · see citing works

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

Ang, K. K., Chin, Z. Y., Wang, C., Guan, C., & Zhang, H. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00039 · Cited by 1,264 · see citing works

An EEG-based brain-computer interface for cursor control

Wolpaw, J. R., McFarland, D. J., Neat, G. W., & Forneris, C. A. (1991) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(91)90040-b · Cited by 1,117 · see citing works

Spatial filter selection for EEG-based communication

McFarland, D. J., McCane, L. M., David, S. V., & Wolpaw, J. R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00022-2 · Cited by 937 · see citing works

Multiclass Brain–Computer Interface Classification by Riemannian Geometry

Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172210 · Cited by 878 · see citing works

A novel deep learning approach for classification of EEG motor imagery signals

Tabar, Y. R., & Halici, U. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2560/14/1/016003 · Cited by 859 · see citing works

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

Sakhavi, S., Guan, C., & Yan, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2789927 · Cited by 655 · see citing works

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., & He, B. (2016) · Scientific Reports · doi:10.1038/srep38565 · Cited by 484 · cited 4× · see citing works

Electroencephalographic (EEG) control of three-dimensional movement

McFarland, D. J., Sarnacki, W. A., & Wolpaw, J. R. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/3/036007 · Cited by 454 · see citing works

Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface

Neuper, C., Scherer, R., Wriessnegger, S. C., & Pfurtscheller, G. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.11.015 · Cited by 421 · see citing works

Riemannian Approaches in Brain-Computer Interfaces: A Review

Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420 · see citing works

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines

Lu, N., Li, T., Ren, X., & Miao, H. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2601240 · Cited by 419 · see citing works

Single-trial EEG classification of motor imagery using deep convolutional neural networks

Tang, Z., Li, C., & Sun, S. (2016) · Optik · doi:10.1016/j.ijleo.2016.10.117 · Cited by 299 · see citing works

Riemannian geometry applied to BCI classification

Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15995-4_78 · Cited by 233 · see citing works

A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals

Zhang, Z., Duan, F., Solé‐Casals, J., Dinarès-Ferran, J., Cichocki, A. S., Yang, Z., & Sun, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2895133 · Cited by 193 · see citing works

Parallel convolutional-linear neural network for motor imagery classification

Sakhavi, S., Guan, C., & Yan, S. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362882 · Cited by 139 · see citing works

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

Aznan, N. K. N., Atapour–Abarghouei, A., Bonner, S., Connolly, J. D., Moubayed, N. A., & Breckon, T. P. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852227 · Cited by 114 · see citing works

Discriminative Transfer Learning Using Similarities and Dissimilarities

Lu, Y., Chen, L., Saidi, A. S., Dellandréa, E., & Wang, Y. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2705760 · Cited by 81 · see citing works

A New Method to Generate Artificial Frames Using the Empirical Mode Decomposition for an EEG-Based Motor Imagery BCI

Dinarès-Ferran, J., Ortner, R., Guger, C., & Solé‐Casals, J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00308 · Cited by 57 · see citing works

Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks

Zhang, Q., & Liu, Y. (2018) · arXiv · doi:10.48550/arxiv.1806.07108 · Cited by 57 · see citing works

EEG-based Intention Recognition from Spatio-Temporal Representations via Cascade and Parallel Convolutional Recurrent Neural Networks.

Zhang, D., Yao, L., Zhang, X., Wang, S., Chen, W., & Boots, R. J. (2017) · Cited by 52 · see citing works

Generating Artificial EEG Signals To Reduce BCI Calibration Time

Lotte, F. (2011) · Cited by 30 · see citing works

Optimization of Single-Trial Detection of Event-Related Potentials Through Artificial Trials

Cecotti, H., Marathe, A. R., & Ries, A. J. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2417054 · Cited by 28 · see citing works

Data augmentation in Riemannian space for Brain-Computer Interfaces

Kalunga, E. K., Chevallier, S., & Barthélemy, Q. (2015) · HAL · Cited by 13 · see citing works

Image-based Motor Imagery EEG Classification using Convolutional Neural Network

Yang, T., Phua, K. S., Yu, J., Selvaratnam, T., Toh, V., Ng, W. H., Ang, K. K., & So, R. Q. (2019) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2019.8834598 · Cited by 13 · see citing works

Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols

Freer, D., Deligianni, F., & Yang, G. (2019) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2019.8771079 · Cited by 5 · see citing works

Data from: Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks

Meng, J., Zhang, S., Bekyo, A., Olsoe, J., Baxter, B., & He, B. (2016) · Data Archiving and Networked Services (DANS) · doi:10.5061/dryad.nh109 · Cited by 4 · see citing works

Toward Real-time BCI Control of Assistive Robots: A Comparison of State-of-the-Art Methods

Freer, D., Ma, Y., & Yang, G. (2018) · Hamlyn Symposium on Medical Robotics (HSMR) · doi:10.31256/hsmr2018.28 · Cited by 2 · see citing works

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

Bashivan, P., Rish, I., Yeasin, M., & Codella, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06448

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S. U., Altuwaijri, G. A., Abdul, W., Bencherif, M. A., & Faisal, M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06352-5 · Cited by 601 · see citing works

Data augmentation for deep-learning-based electroencephalography

Lashgari, E., Liang, D., & Maoz, U. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108885 · Cited by 398 · see citing works

CTNet: a convolutional transformer network for EEG-based motor imagery classification

Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-71118-7 · Cited by 191 · see citing works

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

Li, Y., Guo, L., Liu, Y., Liu, J., & Meng, F. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3099908 · Cited by 172 · see citing works

Data augmentation for motor imagery signal classification based on a hybrid neural network.

Zhang, K., Xu, G., Han, Z., Ma, K., Zheng, X., Chen, L., Duan, N., & Zhang, S. (2020) · Sensors · doi:10.3390/s20164485 · Cited by 130 · see citing works

Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

He, C., Liu, J., Zhu, Y., & Du, W. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.765525 · Cited by 105 · see citing works

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

Xu, L., Xu, M.-P., Jung, T., & Dong, M. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09676-z · Cited by 90 · see citing works

Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics

Li, R., Wang, L., Suganthan, P. N., & Sourina, O. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185587 · Cited by 82 · see citing works

An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification

Wang, X., Liesaputra, V., Liu, Z., Wáng, Y., & Huang, Z. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102738 · Cited by 74 · see citing works

Synthetic Biological Signals Machine-Generated by GPT-2 Improve the Classification of EEG and EMG Through Data Augmentation

Bird, J. J., Pritchard, M., Fratini, A., Ekárt, A., & Faria, D. R. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3056355 · Cited by 68 · see citing works

A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

Tawhid, M. N. A., Siuly, S., & Li, T. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3217515 · Cited by 63 · see citing works

MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain–Machine Interfaces With EEG Channel Selection

Wang, X., Hersche, M., Magno, M., & Luca Benini, C. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3353146 · Cited by 61 · see citing works

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Ko, W., Jeon, E., Jeong, S., Phyo, J., & Suk, H. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.643386 · Cited by 58 · see citing works

CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network

Guo, L., Yu, T., Zhao, S., Xiaoli Li, A., Liao, X., & Li, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3322275 · Cited by 63 · see citing works

A Survey on Deep Learning Classification Algorithms for Motor Imagery

Guragai, B., AlShorman, O., Masadeh, M., & Heyat, M. B. B. (2020) · doi:10.1109/icm50269.2020.9331503 · Cited by 50 · see citing works

A multiscale feature fusion network based on attention mechanism for motor imagery EEG decoding

Gao, D., Yang, W. T., Li, P., Liu, S., Liu, T., Wang, M., & Zhang, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111129 · Cited by 50 · see citing works

Data augmentation strategies for EEG-based motor imagery decoding

George, O., Smith, R. O., Madiraju, P., Yahyasoltani, N., & Ahamed, S. I. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e10240 · Cited by 45 · see citing works

fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy

Nagasawa, T., Sato, T., Nambu, I., & Wada, Y. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6cb9 · Cited by 41 · see citing works

Decoding Finger Tapping With the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution

Lee, M., Jeong, J.-H., Kim, Y., & Lee, S. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3087506 · Cited by 36 · see citing works

Multi-Source Decentralized Transfer for Privacy-Preserving BCIs

Zhang, W., Wang, Z., & Wu, D. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3207494 · Cited by 33 · see citing works

Generalized Contrastive Partial Label Learning for Cross-Subject EEG-Based Emotion Recognition

Li, W., Fan, L., Shao, S., & Song, A. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3398103 · Cited by 29 · see citing works

Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions

Ahuja, C. K., & Sethia, D. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1421922 · Cited by 31 · see citing works

Channel reflection: Knowledge-driven data augmentation for EEG-based brain–computer interfaces

Wang, Z., Li, S., Luo, J., Liu, J., & Wu, D. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106351 · Cited by 28 · see citing works

Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning

Sommer, N. M., Kakillioglu, B., Grant, T., Velipasalar, S., & Hirshfield, L. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3115405 · Cited by 24 · see citing works

SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification

Lee, B.-H., Jeong, J.-H., & Lee, S. (2020) · IEEE Access · doi:10.1109/access.2020.3011140 · Cited by 24 · see citing works

Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification

Han, J., Gu, X., Yang, G., & Lo, B. P. L. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3337072 · Cited by 22 · see citing works

Diffusion models-based motor imagery EEG sample augmentation via mixup strategy

Luo, T.-J., & Cai, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125585 · Cited by 22 · see citing works

A multi-view CNN encoding for motor imagery EEG signals

Zhang, J., & Li, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105063 · Cited by 22 · see citing works

MSFNet: A Multi-Scale Space-Time Frequency Fusion Network for Motor Imagery EEG Classification

Wang, C., Wu, Y., Wang, C., Ren, Y., Shen, J., Pang, T., Chan, C. S., Ren, W., & Yu, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3351204 · Cited by 20 · see citing works

A Local-Ascending-Global Learning Strategy for Brain-Computer Interface

Gao, D., Zhang, H., Li, P., Tang, T., Liu, S., Zhou, Z., Ying, S., Zhu, Y., & Zhang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28867 · Cited by 20 · see citing works

Objective Pain Assessment Using Deep Learning Through EEG-Based Brain–Computer Interfaces

Al-Nafjan, A., Alshehri, H., & Aldayel, M. S. (2025) · Biology · doi:10.3390/biology14020210 · Cited by 19 · see citing works

Deep-Learning-Based Automatic Selection of Fewest Channels for Brain–Machine Interfaces

Kim, H.-S., Ahn, M.-H., & Min, B. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3052813 · Cited by 18 · see citing works

Studies to Overcome Brain–Computer Interface Challenges

Choi, W.-S., & Yeom, H. G. (2022) · Applied Sciences · doi:10.3390/app12052598 · Cited by 20 · see citing works

Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network

Echtioui, A., Zouch, W., Ghorbel, M., Mhiri, C., & Hamam, H. (2023) · Clinical EEG and Neuroscience · doi:10.1177/15500594221148285 · Cited by 18 · see citing works

R-WGAN-Based Multitimescale Enhancement Method for Predicting f-CaO Cement Clinker

Hao, X., Liu, L., Huang, G., Zhang, Y., Zhang, Y., & Dang, H. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3126832 · Cited by 18 · see citing works

Multi-Tiered CNN Model for Motor Imagery Analysis: Enhancing UAV Control in Smart City Infrastructure for Industry 5.0

Al-Qaysi, Z. T., Salih, M. M., Shuwandy, M. L., Ahmed, M. A., & Altarazi, Y. S. M. (2023) · Applied Data Science and Analysis · doi:10.58496/adsa/2023/007 · Cited by 17 · see citing works

Deep learning-based electroencephalic diagnosis of tinnitus symptom

Hong, E.-S., Kim, H.-S., Hong, S. K., Pantazis, D., & Min, B. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1126938 · Cited by 16 · see citing works

Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery

Al-Qaysi, Z. T., Albahri, A. S., Ahmed, M. A., & Muhammed, S. M. (2023) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-023-01316-6 · Cited by 15 · see citing works

WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks

Sun, Z., Li, B., Duan, F., Jia, H., Wang, S., Liu, Y., Cichocki, A. S., Caiafa, C. F., & Solé‐Casals, J. (2020) · IEEE Access · doi:10.1109/access.2020.3044732 · Cited by 15 · see citing works

Active poisoning: efficient backdoor attacks on transfer learning-based brain-computer interfaces

Jiang, X., Meng, L., Li, S., & Wu, D. (2023) · Science China Information Sciences · doi:10.1007/s11432-022-3548-2 · Cited by 14 · see citing works

Human-robot collaborative disassembly enabled by brainwaves and improved generative adversarial network

Hu, Y., Li, W., Zhou, Y. Q., & Pham, D. T. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102434 · Cited by 13 · see citing works

A conditional input-based GAN for generating spatio-temporal motor imagery electroencephalograph data

Raoof, I., & Gupta, M. K. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08927-w · Cited by 13 · see citing works

Improving Error Related Potential Classification by using Generative Adversarial Networks and Deep Convolutional Neural Networks

Gao, C., Li, Z., Ora, H., & Miyake, Y. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313238 · Cited by 13 · see citing works

A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions

Khademi, S., Neghabi, M., Farahi, M., Shirzadi, M., & Marateb, H. R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91197-9.00004-7 · Cited by 13 · see citing works

Effect of natural and synthetic noise data augmentation on physical action classification by brain–computer interface and deep learning

Gordienko, Y. G., Gordienko, N., Taran, V., Rojbi, A., Telenyk, S., & Stirenko, S. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1521805 · Cited by 12 · see citing works

Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization

Zheng, Y., Wu, S., Chen, J., Yao, Q., & Zheng, S. (2025) · Bioengineering · doi:10.3390/bioengineering12050495 · Cited by 12 · see citing works

Riemannian Channel Selection for BCI With Between-Session Non-Stationarity Reduction Capabilities

Sadatnejad, K., & Lotte, F. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3167262 · Cited by 12 · see citing works

A time-frequency map generation network embedded with spectral self-attention for motor imagery classification

Niu, X., Lü, N., Yan, R., & Luo, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106206 · Cited by 12 · see citing works

MI-MBFT: Superior Motor Imagery Decoding of Raw EEG Data Based on a Multibranch and Fusion Transformer Framework

Luo, J., Cheng, Q., Wang, H., Du, Q., Wang, Y., & Li, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3445971 · Cited by 10 · see citing works

ATGAN: attention-based temporal GAN for EEG data augmentation in personal identification

Zhang, S., Sun, L., Mao, X., Zhao, M., & Hu, Y. (2024) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-024-01188-2 · Cited by 10 · see citing works

Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks

Shi, Y., Jiang, A., Zhong, J., Li, M., & Zhu, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3496757 · Cited by 10 · see citing works

Normalized deep learning algorithms based information aggregation functions to classify motor imagery EEG signal

Al-Hamadani, A. A., Mohammed, M. J., & Tariq, S. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08944-9 · Cited by 10 · see citing works

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods

You, Z., Guo, Y., Zhang, X., & Zhao, Y. (2025) · Sensors · doi:10.3390/s25103178 · Cited by 9 · see citing works

Data-driven Data Augmentation for Motor Imagery Brain-Computer Interface

Lee, H. K., Lee, J.-H., Park, J.-O., & Choi, Y.-S. (2021) · International Conference on Information Networking · doi:10.1109/icoin50884.2021.9333908 · Cited by 8 · see citing works

Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires

Collazos-Huertas, D. F., Velasquez-Martinez, L. F., Pérez-Nastar, H. D., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2021) · Sensors · doi:10.3390/s21155105 · Cited by 8 · see citing works

Improving cross-session motor imagery decoding performance with data augmentation and domain adaptation

Guo, S., Wang, Y., Liu, Y., Zhang, X., & Tang, B. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107756 · Cited by 7 · see citing works

Domain-independent short-term calibration based hybrid approach for motor imagery electroencephalograph classification: a comprehensive review

Raoof, I., & Gupta, M. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15900-1 · Cited by 7 · see citing works

Bispectrum-based hybrid neural network for motor imagery classification

Liu, C., Jin, J., Daly, I. P., Sun, H., Huang, Y., Wang, X., & Cichocki, A. S. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109593 · Cited by 6 · see citing works

Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification

Niu, X., Lü, N., Yan, R., & Luo, H. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3386565 · Cited by 6 · see citing works

CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration

Eissa, T., Huber, M., Obermayer-Pietsch, B. M., Linkohr, B., Peters, A., Fleischmann, F., & Žigman, M. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae449 · Cited by 6 · see citing works

Gated parallel feature fusion multi-task learning for motor imagery EEG classification

Wang, X., Liesaputra, V., & Huang, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128678 · Cited by 5 · see citing works

A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability

Zhang, Y., Zhang, C., Jiang, R., Qiu, S., & He, H. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3527629 · Cited by 4 · see citing works

Stroke Motor Imagery Recognition based on EEG

Sun, Y., Li, J., Dong, G., Wang, Y., Li, H., Yang, Q., Guo, D., Zhang, Y., Leng, J., & Xu, F. (2021) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict53123.2021.9531188 · Cited by 4 · see citing works

Deep learning-based electroencephalic decoding of the phase-lagged transcranial alternating current stimulation

Kwon, J., & Min, B. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1545726 · Cited by 3 · see citing works

LGFCNN: A synergistic framework integrating graph-based spatial filter and lightweight CNN for SSVEP recognition

Ma, R., Cao, Y. K., Xie, S. Q., Zhang, M., Li, J., & Zhang, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131561 · Cited by 3 · see citing works

Data alignment based adversarial defense benchmark for EEG-based BCIs

Chen, X., Jia, T., & Wu, D. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107516 · Cited by 3 · see citing works

Distributionally Robust Cross Subject EEG Decoding

Duan, T., Wang, Z., Doretto, G., Li, F., Tao, C., & Adjeroh, D. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230318 · Cited by 3 · see citing works

Multi-model ensemble for motor imagery classification using factorization machine

Wen, K. (2022) · International Conference on Advanced Algorithms and Neural Networks (AANN) · doi:10.1117/12.2637171 · Cited by 3 · see citing works

A Lightweight Dual-Attention Neural Network for Robust and Efficient EEG Motor Imagery Decoding

Wang, G., Song, X., Jiang, L., Zhang, Y., Yao, D., Lu, J., Xu, P., & Li, F. (2026) · International Journal of Neural Systems · doi:10.1142/s0129065726500267 · Cited by 2 · see citing works

NIRS Data Augmentation Technique to Detect Hemodynamic Peaks During Self-Paced Motor Imagery

Ⅴ, Z. P., Paik, S.-H., Lee, S. H., Choi, E.-J., & Kim, B. (2023) · IEEE Access · doi:10.1109/access.2023.3263489 · Cited by 2 · see citing works

Motor imagery EEG classification method: based on a novel BiLSTM-Attention-CNN hybrid neural network

Chu, H., & Zhang, C. (2022) · International Conference on Signal Image Processing and Communication (ICSIPC) · doi:10.1117/12.2643730 · Cited by 2 · see citing works

ADAPTER: Auto-Augmentation for Emotion Recognition in EEG - A Class and Subject Invariant Approach

Ahuja, C. K., & Setia, D. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10308153 · Cited by 1 · see citing works

The detection method of continuous outliers in complex network data streams based on C-LSTM

Shu, Z., & Li, X. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02475-9 · Cited by 1 · see citing works

The Impact of Non-Task-Related Neural Activity in EEG-Based Motor Imagery Classification

Bove, S., Giaquinto, M., Percannella, G., Saggese, A., & Vento, M. (2025) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine66377.2025.11340334 · Cited by 1 · see citing works

Bridging the state gap: Multifractal Conditional Diffusion with Latent Feature Hallucination for physiologically consistent EEG

Shen, C., & Namiki, A. (2026) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2026.118384 · Cited by 1 · see citing works

EEG-VAE: A Variational Autoencoder for EEG Data Augmentation and Classification

Li, Y., Zhao, Z., Chen, Z., Zhang, Y., & Yu, Y. (2025) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait67930.2025.11522647 · Cited by 1 · see citing works

Method for automatic detection of movement-related EEG pattern time boundaries

Shcherban, I., Lazurenko, D., Shcherban, O., Shaposhnikov, D., Kirilenko, N. E., & Shustova, A. V. (2023) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-023-08837-y · Cited by 1 · see citing works

Cross-Site Generalization for Imbalanced Epileptic Classification

Abdallah, T., Jrad, N., Abdallah, F., Humeau‐Heurtier, A., & Bogaert, P. V. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094829 · Cited by 1 · see citing works

Multi-Tire CNN Model for Motor Imagery Based UAV ControlPreprint

Alqaysi, Z., Shuwandy, M. L., Ahmed, M. S., Salih, M., & Altarazi, Y. S. M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4162650 · Cited by 1 · see citing works

MIndGrasp: A New Training and Testing Framework for Motor Imagery Based 3-Dimensional Assistive Robotic ControlPreprint

Freer, D., & Yang, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00369 · Cited by 1 · see citing works

Adaptive Hausdorff Estimation of Movement-Related Eeg Patterns for Brain-Computer InterfacesPreprint

Щербань, И. В., Lazurenko, D. M., Shaposhnikov, D. G., Kirilenko, N. E., Shustova, A. V., & Kiroy, V. N. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3915013

Development of Algorithms to Detect EEG Patterns Specific for Arbitrary Motor Activity of a Human in the BCI Applications

Щербань, И. В., Lazurenko, D. M., Shaposhnikov, D. G., & Kirilenko, N. E. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-91581-0_10

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Handayani, T. P., Sudiro, S. A., Andayani, R., & Madenda, S. (2022) · International Conference of Computer Science and Information Technology (ICoSNIKOM) · doi:10.1109/icosnikom56551.2022.10034902

Analysis of Window-Delay Score for Data Augmentation Methods in Brain-Computer Interfaces

Maurício, J. S. S., Amorim, M. M., Vieira, A. B., Bernardino, H. S., & de Souza, G. H. (2024) · doi:10.5753/eniac.2024.245225

An EEG Dataset of Word-level Brain Responses for Semantic Text Relevance

Gryshchuk, V., Spapé, M., Maistro, M., Lioma, C., & Ruotsalo, T. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730289

A Spatial-Temporal Attention-Based Multi-Scale Feature Extraction Network for Motor Imagery Decoding

Ren, Z., & Qi, P. (2025) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2025.3617978

Self-organized phase-locked component pair learning in motor imagery classification

Niu, X., Lü, N., Luo, H., & Yan, R. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114250

Data Distribution Evolution for Robust EEG Emotion Recognition With Limited Data Resource

Duan, T., Wang, Z., Shen, L., Niu, S., Doretto, G., Adjeroh, D., & Tao, C. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3684827

Feature-Space Conditional Data Augmentation for Motor Imagery EEG Classification

Chen, Z., Zhang, Y., Hu, J., Yu, Y., & Zeng, L. (2026) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp69961.2026.11540908

Improved PCNN with transfer learning for IoT based rice leaf disease detection with Modified Trans U-net segmentation

Subburaman, G., & Selvadurai, M. V. (2026) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2026.102154

Multimodal emotion recognition using hybrid deep learning architecture

Kothari, N. S., More, S. A., & Jadhav, D. N. S. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116761

Human–Robot Collaborative Disassembly Enabled by Brainwaves and Improved Generative Adversarial Network

Li, W., Hu, Y., Zhou, Y., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_10

DecodeMI-Net: A Multi-Perspective Deep Neural Network With Channel-Wise Attention for Motor Imagery EEG Decoding

Deb, A., Shahnaz, C., Fattah, S. A., & Saquib, M. (2026) · IEEE Access · doi:10.1109/access.2026.3727566

cGAN-Based Data Augmentation in Latent Space for Seismology

Luders-Ledezma, R., Pino, O. V., & Yoma, N. B. (2026) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2026.3732281

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Ozelbas, E., Tülay, E. E., & Özekes, S. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad200c · Cited by 5 · see citing works

Combined CNN and LSTM for Motor Imagery Classification

Lu, P., Gao, N., Lu, Z., Yang, J., Bai, O., & Li, Q. (2019) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei48845.2019.8965653 · Cited by 32 · see citing works

AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification

Wang, S., Wang, R., Chang, L., Wu, J., & Hu, L. (2026) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2025.1704111 · Cited by 1 · see citing works

Classify Motor Imagery by a Novel CNN with Data Augmentation

Huang, W., Wang, L., Yan, Z., & Liu, Y. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176361 · Cited by 19 · see citing works

Data-driven Data Augmentation for Motor Imagery Brain-Computer Interface

Lee, H. K., Lee, J.-H., Park, J.-O., & Choi, Y.-S. (2021) · International Conference on Information Networking · doi:10.1109/icoin50884.2021.9333908 · Cited by 8 · see citing works