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 · 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

Adam: A Method for Stochastic Optimization

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

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

A Mathematical Theory of Communication

Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 49,574 · see citing works

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 · see citing works

Untitled work

(2014) · Cited by 33,419 · see citing works

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 · see citing works

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 · see citing works

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 14,474 · see citing works

Untitled work

(2010) · Cited by 14,134 · see citing works

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works

Selective Search for Object Recognition

Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works

Proceedings European Conference on Computer Vision 2002

Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153 · see citing works

Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.

Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228 · see citing works

Improving deep neural networks for LVCSR using rectified linear units and dropout

Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639346 · Cited by 1,310 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky (2012) · Medical Entomology and Zoology · Cited by 799 · see citing works

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 · see citing works

BCI competition III: Dataset II—Ensemble of SVMs for BCI P300 speller

Rakotomamonjy, A., & Guigue, V. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.915728 · Cited by 512 · see citing works

BCI Competition 2003—Data Sets Ib and IIb: Feature Extraction From Event-Related Brain Potentials With the Continuous Wavelet Transform and the $hboxtt t$ -Value Scalogram

Bostanov, V. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.826702 · Cited by 347 · see citing works

Sleep Stage Classification Using Unsupervised Feature Learning

Längkvist, M., Karlsson, L., & Loutfi, A. (2012) · Advances in Artificial Neural Systems · doi:10.1155/2012/107046 · Cited by 248 · see citing works

Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach

Li, Y., Ma, Z., Lu, W., & Li, Y. (2006) · Physiological Measurement · doi:10.1088/0967-3334/27/4/008 · Cited by 196 · see citing works

Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement

Wulsin, D., Gupta, J. R., Mani, R., Blanco, J. A., & Litt, B. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/036015 · Cited by 186 · see citing works

An adaptive P300-based control system

Jin, J., Allison, B. Z., Sellers, E. W., Brunner, C., Horki, P., Wang, X., & Neuper, C. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/036006 · Cited by 159 · see citing works

Bayesian Machine Learning: EEG\/MEG signal processing measurements

Wu, W., Nagarajan, S. S., & Chen, Z. S. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2481559 · Cited by 116 · see citing works

Untitled work

(2014) · neural information processing systems · Cited by 66 · see citing works

An Online Semi-supervised Brain–Computer Interface

Gu, Z., Yu, Z., Shen, Z., & Li, Y. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2261994 · Cited by 53 · see citing works

An Idle-State Detection Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Maximum Evoked Response Spatial Filter

Zhang, D., Huang, B., Wu, W., & Li, S. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500306 · Cited by 39 · see citing works

An N200 speller integrating the spatial profile for the detection of the non-control state

Zhang, D., Song, H., Xu, H., Wu, W., Gao, S., & Hong, B. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/2/026016 · Cited by 35 · 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

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 · see citing works

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 · see citing works

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 272 · see citing works

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 277 · see citing works

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 · see citing works

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 · see citing works

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 233 · see citing works

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 221 · see citing works

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 · see citing works

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 · see citing works

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 194 · see citing works

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 · see citing works

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 142 · see citing works

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 133 · see citing works

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 · see citing works

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 121 · see citing works

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 101 · 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

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 · see citing works

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 83 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 61 · see citing works

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 · see citing works

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 57 · see citing works

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 56 · see citing works

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 · see citing works

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 57 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 45 · see citing works

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 · see citing works

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 · see citing works

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 42 · see citing works

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 · see citing works

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 · see citing works

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 38 · see citing works

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 38 · see citing works

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 · see citing works

Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification

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

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 · see citing works

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 · see citing works

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 · see citing works

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 37 · see citing works

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 · see citing works

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 34 · see citing works

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 32 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 27 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 26 · see citing works

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 · see citing works

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 · see citing works

3D Input Convolutional Neural Networks for P300 Signal Detection

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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 21 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 18 · see citing works

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 18 · see citing works

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 18 · see citing works

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 · see citing works

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 17 · see citing works

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 17 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · 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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Fusion of Convolutional Neural Networks for P300 Based Character Recognition

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

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 · see citing works

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 · see citing works