Deep learning based on Batch Normalization for P300 signal detection

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

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

A Mathematical Theory of Communication

Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Untitled work

(2014) · Cited by 33,419

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475

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

Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition

Zhang, D., Yao, L., Chen, K., Wang, S., Chang, X., & Liu, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2905157 · Cited by 367

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296

Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning

Zang, H., Cheng, L., Ding, T., Cheung, K. W., Wei, Z., & Sun, G. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105790 · Cited by 282

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taïar, R., Hancock, P. A., & Aljuaid, A. M. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 295

Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines

Wang, J., Li, S., An, Z., Jiang, X., Qian, W., & Ji, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.049 · Cited by 242

Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks

Lei, X., Sun, L., & Xia, Y. (2021) · Structural Health Monitoring · doi:10.1177/1475921720959226 · Cited by 236

Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface

Fahimi, F., Dosen, S. S., Ang, K. K., Mrachacz‐Kersting, N., & Guan, C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3016666 · Cited by 244

EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces

Santamaría-Vázquez, E., Martínez-Cagigal, V., Vaquerizo-Villar, F., & Hornero, R. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3048106 · Cited by 240

Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification

Xu, B., Li, Z., Song, A., Wu, C., Li, W., Zhang, D., Xu, G., Li, H., & Zeng, H. (2018) · IEEE Access · doi:10.1109/access.2018.2889093 · Cited by 204

Fault classification in power system distribution network integrated with distributed generators using CNN

Rai, P., Londhe, N. D., & Raj, R. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106914 · Cited by 193

Deep Learning in the Biomedical Applications: Recent and Future Status

Zemouri, R. M., Noureddine Zerhouni, A., & Racoceanu, D. (2019) · Applied Sciences · doi:10.3390/app9081526 · Cited by 200

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

Borra, D., Fantozzi, S., & Magosso, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.05.032 · Cited by 142

SR2CNN: Zero-Shot Learning for Signal Recognition

Dong, Y., Jiang, X., Zhou, H., Lin, Y., & Shi, Q. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3070186 · Cited by 144

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Gong, S., Xing, K., Cichocki, A., & Li, J. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3079712 · Cited by 135

Wearable EEG electronics for a Brain–AI Closed-Loop System to enhance autonomous machine decision-making

Shin, J. H., Kwon, J., Kim, J. U., Ryu, H., Ok, J., Joon Kwon, S., Park, H., & Kim, T.-I. (2022) · npj Flexible Electronics · doi:10.1038/s41528-022-00164-w · Cited by 123

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Rakhmatulin, I., Dao, M.-S., Nassibi, A., & Mandic, D. P. (2024) · Sensors · doi:10.3390/s24030877 · Cited by 147

A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning

Feng, L., He, F., Wang, F., Zhang, D., Xia, Y., & Li, X. (2020) · Applied Sciences · doi:10.3390/app10051605 · Cited by 103

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

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization

Farahat, A., Reichert, C., Sweeney-Reed, C. M., & Hinrichs, H. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab3bb4 · Cited by 92

A multi-scale convolutional neural network based fault diagnosis model for complex chemical processes

Song, Q., & Jiang, P. (2021) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2021.11.020 · Cited by 85

Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

Alhanaf, A. S., Balık, H. H., & Farsadi, M. (2023) · Energies · doi:10.3390/en16227680 · Cited by 83

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

Huang, W., Xue, Y., Hu, L., & Liuli, H. (2020) · IEEE Access · doi:10.1109/access.2020.3009665 · Cited by 82

Deep Fusion Feature Learning Network for MI-EEG Classification

Yang, J. L., Yao, S., & Wang, J. (2018) · IEEE Access · doi:10.1109/access.2018.2877452 · Cited by 77

ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement

Wang, Z., Chen, C., Li, J., Wan, F., Sun, Y., & Wang, H. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3237319 · Cited by 75

MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller

Kundu, S., & Ari, S. (2019) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2019.2959559 · Cited by 69

A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential

Zhang, X., Xu, G., Mou, X., Ravi, A., Li, M., Wang, Y., & Jiang, N. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2914904 · Cited by 65

Image presentation and effective classification of odor intensity levels using multi-channel electronic nose technology combined with GASF and CNN

Xiong, L., He, M., Hu, C., Hou, Y., Han, S., & Tang, X. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.134492 · Cited by 64

Determination of alcohols-diesel oil by near infrared spectroscopy based on gramian angular field image coding and deep learning

Liu, S., Wang, S., Hu, C., & Bi, W. (2021) · Fuel · doi:10.1016/j.fuel.2021.122121 · Cited by 65

A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids

Aslam, Z., Javaid, N., Ahmad, A., Ahmed, A., & Gulfam, S. M. (2020) · Energies · doi:10.3390/en13215599 · Cited by 60

Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning

Kshirsagar, G. B., & Londhe, N. D. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2875024 · Cited by 59

On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications

Podmore, J. J., Breckon, T. P., Aznan, N. K. N., & Connolly, J. D. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2904791 · Cited by 59

Temporal-spatial-frequency depth extraction of brain-computer interface based on mental tasks

Wang, L., Huang, W., Yang, Z., & Zhang, C. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101845 · Cited by 55

A survey of deep learning models in medical therapeutic areas

Nogales, A., García-Tejedor, Á. J., Martín, D. M., Vara, J. S., & Rodríguez, C. A. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102020 · Cited by 59

A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs

Zang, B., Lin, Y., Liu, Z., & Gao, X. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac1610 · Cited by 54

A Simple Convolutional Neural Network for Accurate P300 Detection and Character Spelling in Brain Computer Interface

Shan, H., Liu, Y., & Stefanov, T. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/222 · Cited by 53

A systematic deep learning model selection for p300-based brain-computer interfaces

Abibullaev, B., & Zollanvari, A. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3051136 · Cited by 53

P300 based character recognition using convolutional neural network and support vector machine

Kundu, S., & Ari, S. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101645 · Cited by 52

Deep Learning‐Based Single‐Cell Optical Image Studies

Sun, J., Tárnok, A., & Su, X. (2020) · Cytometry Part A · doi:10.1002/cyto.a.23973 · Cited by 51

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Li, M., Li, F., Pan, J., Zhang, D., Zhao, S., Li, J., & Wang, F. (2021) · Sensors · doi:10.3390/s21051613 · Cited by 50

BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks

Xu, M., Chen, Y., Wang, Y., Wang, D., Liu, Z., & Zhang, L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3145515 · Cited by 44

Black-Box Modeling of DC–DC Converters Based on Wavelet Convolutional Neural Networks

Rojas-Dueñas, G., Riba, J., & Moreno‐Eguilaz, M. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3098377 · Cited by 43

A spatiotemporal deep learning method for excavation-induced wall deflections

Tao, Y., Zeng, S., Sun, H., Cai, Y., Zhang, J., & Pan, X. (2024) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.09.034 · Cited by 44

Brain-Computer Interface Speller System for Alternative Communication: A Review

Kundu, S., & Ari, S. (2021) · IRBM · doi:10.1016/j.irbm.2021.07.001 · Cited by 42

Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection

Zhang, Z., Chen, G., & Yang, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3083710 · Cited by 40

Intelligent Generation Method of Innovative Structures Based on Topology Optimization and Deep Learning

Wang, Y., Du, W., Hui, W., & Zhao, Y. (2021) · Materials · doi:10.3390/ma14247680 · Cited by 39

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Gutiérrez-Martínez, J., Mercado-Gutierrez, J. A., Carvajal-Gámez, B. E., Rosas‐Trigueros, J. L., & Contreras-Martinez, A. E. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.772837 · Cited by 41

A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision

Borra, D., Fantozzi, S., & Magosso, E. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.655840 · Cited by 36

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification

Zhang, Z., He, Y. L., Mai, W., Luo, Y., Li, X., Cheng, Y., Huang, X., & Lin, R. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3437676 · Cited by 37

Analysis and classification of hybrid BCI based on motor imagery and speech imagery

Wang, L., Liu, X., Liang, Z., Yang, Z., & Hu, X. (2019) · Measurement · doi:10.1016/j.measurement.2019.07.070 · Cited by 37

A few filters are enough: Convolutional neural network for P300 detection

Alvarado-González, M., Fuentes-Pineda, G., & Cervantes‐Ojeda, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.104 · Cited by 37

Subject-specific mental workload classification using EEG and stochastic configuration network (SCN)

Pang, L., Guo, L., Zhang, J., Wanyan, X., Qu, H., & Wang, X. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102711 · Cited by 37

Deep Learning with ConvNet Predicts Imagery Tasks Through EEG

Altan, G., Yayık, A., & Kutlu, Y. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10533-7 · Cited by 41

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Li, F., Li, X., Wang, F., Zhang, D., Xia, Y., & He, F. (2020) · Applied Sciences · doi:10.3390/app10041546 · Cited by 34

Weighted Ensemble of Deep Convolution Neural Networks for Single-Trial Character Detection in Devanagari-Script-Based P300 Speller

Kshirsagar, G. B., & Londhe, N. D. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2942437 · Cited by 35

Deep learning-based EEG analysis: investigating P3 ERP components

Borra, D., & Magosso, E. (2021) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2004083 · Cited by 34

Brain-Computer Interface System Based on P300 Processing with Convolutional Neural Network, Novel Speller, and Low Number of Electrodes

Ramírez-Quintana, J. A., Madrid-Herrera, L., Chacon-Murguia, M. I., & Corral-Martínez, L. F. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09744-2 · Cited by 31

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

Zhang, H., Wang, Z., Yu, Y., Yin, H., Chen, C., & Wang, H. (2022) · Brain Science Advances · doi:10.26599/bsa.2022.9050007 · Cited by 30

Deep topology network: A framework based on feedback adjustment learning rate for image classification

Fan, L., Zhang, T., Zhao, X. W., Wang, H., & Zheng, M. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100935 · Cited by 28

Convolutional Neural Network for a P300 Brain-Computer Interface to Improve Social Attention in Autistic Spectrum Disorder

Borra, D., Fantozzi, S., & Magosso, E. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-31635-8_223 · Cited by 28

Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

Zheng, G., Sun, W., Zhang, H., Zhou, Y., & Gao, C. (2021) · Eksploatacja i Niezawodnosc - Maintenance and Reliability · doi:10.17531/ein.2021.4.3 · Cited by 26

A Hybrid BCI Integrating EEG and Eye-Tracking for Assisting Clinical Communication in Patients With Disorders of Consciousness

Yi, Z., Pan, J., Chen, Z., Lu, D., Cai, H., Li, J., & Xie, Q. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3435016 · Cited by 26

A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

Venuto, D. D., & Mezzina, G. (2021) · Sensors · doi:10.3390/s21123961 · Cited by 26

Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms

Martínez-Cagigal, V., Santamaría-Vázquez, E., & Hornero, R. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108176 · Cited by 27

Single-trial stimuli classification from detected P300 for augmented Brain–Computer Interface: A deep learning approach

Leoni, J., Strada, S. C., Tanelli, M., Brusa, A., & Proverbio, A. M. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100393 · Cited by 27

Multiple Scale Convolutional Few-Shot Learning Networks for Online P300-Based Brain–Computer Interface and Its Application to Patients With Disorder of Consciousness

Pan, J., Cai, H., Huang, H., He, Y., & Li, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3267367 · Cited by 26

Single Trial P300 Classification Using Convolutional LSTM and Deep Learning Ensembles Method

Joshi, R. B., Goel, P., Sur, M., & Murthy, H. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04021-5_1 · Cited by 26

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968360 · Cited by 25

A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding

Lin, B., Deng, S., Gao, H., & Yin, J. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3024228 · Cited by 25

A Support Vector Neural Network for P300 EEG Signal Classification

Zhang, Z., Chen, G., & Chen, S. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3105493 · Cited by 30

P300 event-related potential detection using one-dimensional convolutional capsule networks

Liu, X., Xie, Q., Lv, J., Huang, H., & Wang, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114701 · Cited by 23

Finite-time-convergent support vector neural dynamics for classification

Liu, M., Jiang, Q., Li, H., Cao, X., & Lv, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128810 · Cited by 23

Improving accuracy of cavitation severity recognition in axial piston pumps by denoising time–frequency images

Chao, Q., Wei, X., Lei, J., Tao, J., & Liu, C. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac491d · Cited by 23

P300 based character recognition using sparse autoencoder with ensemble of SVMs

Kundu, S., & Ari, S. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.08.001 · Cited by 21

A Deep Learning Architecture for P300 Detection with Brain-Computer Interface Application

Kundu, S., & Ari, S. (2019) · IRBM · doi:10.1016/j.irbm.2019.08.001 · Cited by 22

A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection

Wang, H., Wang, Z., Sun, Y., Yuan, Z., Xu, T., & Li, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3415474 · Cited by 20

Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model

Yu, S., Wang, Z., Wang, F., Chen, K., Yao, D., Xu, P., Zhang, Y., Wang, H., & Zhang, T. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhad511 · Cited by 20

Intelligent design of key joints in aerial building machine using topology optimization and generative adversarial network

Xia, Z., Wang, J., Li, Y., Zhang, L., & Liu, C. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105747 · Cited by 19

Performance improvement of P300-based home appliances control classification using convolution neural network

Shukla, P. K., Chaurasiya, R. K., & Verma, S. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102220 · Cited by 19

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging

Lee, S., Oh, O., Kim, D., Kim, D., Hussey, D. S., Wang, G., & Lee, S. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-66690-7 · Cited by 18

E2ENNet: An end-to-end neural network for emotional brain-computer interface

Han, Z., Chang, H., Zhou, X., Wang, J., Wang, L., & Shao, Y. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.942979 · Cited by 21

Subject-Independent Wearable P300 Brain–Computer Interface Based on Convolutional Neural Network and Metric Learning

Hu, L., Gao, W., Lu, Z., Shan, C., Ma, H., Zhang, W., & Li, Y. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3457502 · Cited by 21

Linking ADHD and Behavioral Assessment Through Identification of Shared Diagnostic Task-Based Functional Connections

McNorgan, C., Judson, C., Handzlik, D., & Holden, J. G. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.583005 · Cited by 20

Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN

Du, P., Li, P., Cheng, L., Li, X., & Su, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1132290 · Cited by 17

Subject-Independent Classification of P300 Event-Related Potentials Using a Small Number of Training Subjects

Abibullaev, B., Kunanbayev, K., & Zollanvari, A. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3189576 · Cited by 18

Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain—Computer Interfaces

Liang, X., Liu, Y., Yu, Y., Liu, K., Liu, Y., & Zhou, Z. (2023) · Brain Sciences · doi:10.3390/brainsci13020268 · Cited by 19

Emotion Recognition Based on Selected EEG Signals by Common Spatial Pattern

Li, G., Yuan, B., Ouyang, D., Li, W., Pan, Y., Guo, Z., & Guo, G. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3358400 · Cited by 17

HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method

Wu, M., Qin, H., Wan, X., & Du, Y. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3113293 · Cited by 17

Facial Expression Recognition Using a Simplified Convolutional Neural Network Model

Kandeel, A. A., Rahmanian, M., Zulkernine, F. H., Abbas, H. M., & Hassanein, H. S. (2021) · International Conference on Communications, Signal Processing, and their Applications (ICCSPA) · doi:10.1109/iccspa49915.2021.9385739 · Cited by 16

Deep Learning Methods in Electroencephalography

Kotowski, K., Stąpor, K., & Ochab, J. K. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-49724-8_8 · Cited by 16

Asynchronous Video Target Detection Based on Single-Trial EEG Signals

Song, X., Yan, B., Tong, L., Shu, J., & Zeng, Y. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3009978 · Cited by 16

A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces

Aghili, S. N., Kilani, S., Khushaba, R. N., & Rouhani, E. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15380 · Cited by 15

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

A comprehensive review on deep learning techniques for a BCI-based communication system

Bhuvaneshwari, M., Kanaga, E. G. M., Anitha, J., Raimond, K., & George, S. T. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821633-0.00013-1 · Cited by 15

Deep learning architecture based on the combination of convolutional and recurrent layers for ERP-based brain-computer interfaces

Santamaría-Vázquez, E., Martínez-Cagigal, V., Gomez-Pilar, J., & Hornero, R. (2020) · IFMBE Proceedings · doi:10.1007/978-3-030-31635-8_224 · Cited by 15

A new dynamically convergent differential neural network for brain signal recognition

Zhang, Z., Sun, J., & Chen, T. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103130 · Cited by 14

An Interpretable Convolutional Neural Network for P300 Detection: Analysis of Time Frequency Features for Limited Data

Tajmirriahi, M., Amini, Z., Rabbani, H., & Kafieh, R. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3159475 · Cited by 14

An efficient deep learning framework for P300 evoked related potential detection in EEG signal

Havaei, P., Zekri, M., Mahmoudzadeh, E., & Rabbani, H. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107324 · Cited by 14

Enhancing P300-Based Brain-Computer Interfaces with Hybrid Transfer Learning: A Data Alignment and Fine-Tuning Approach

Kilani, S., Aghili, S. N., & Hulea, M. G. (2023) · Applied Sciences · doi:10.3390/app13106283 · Cited by 14

A Convolutional Neural Network based on Batch Normalization and Residual Block for P300 Signal Detection of P300-speller System

Lu, Z., Li, Q., Gao, N., Wang, T., Yang, J., & Bai, O. (2019) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2019.8816214 · Cited by 10

Fusion of Convolutional Neural Networks for P300 Based Character Recognition

Kundu, S., & Ari, S. (2019) · doi:10.1109/icit48102.2019.00034 · Cited by 12

Towards Effective Deep Neural Network Approach for Multi-Trial P300-Based Character Recognition in Brain-Computer Interfaces

Shukla, P. K., Cecotti, H., & Meena, Y. K. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.11169725 · Cited by 1

Spelling Detection based on P300 Signal with Convolutional Neural Network (CNN) Algorithm

Muharrom, K. M. R., & Primartha, R. (2022) · Sriwijaya Journal of Informatics and Applications · doi:10.36706/sjia.v3i1.38

Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces

Cecotti, H., & Graser, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.125 · Cited by 778