Single-trial EEG RSVP classification using convolutional neural networks

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

Deep learning

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

Updating P300: An integrative theory of P3a and P3b

Polich, J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.019 · Cited by 7,919 · see citing works

Improving neural networks by preventing co-adaptation of feature detectors

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382 · 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

Evoked-potential correlates of stimulus uncertainty

Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965) · Science · doi:10.1126/science.150.3700.1187 · Cited by 3,061 · 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

Stochastic Gradient Descent Tricks

Bottou, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_25 · Cited by 2,097 · see citing works

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans

Wolpaw, J. R., & McFarland, D. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403504101 · Cited by 1,512 · see citing works

The mental prosthesis: assessing the speed of a P300-based brain-computer interface

Donchin, E., Spencer, K., & Wijesinghe, R. S. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.847808 · Cited by 1,217 · see citing works

Designing optimal spatial filters for single-trial EEG classification in a movement task

Müller-Gerking, J., Pfurtscheller, G., & Flyvbjerg, H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(98)00038-8 · Cited by 846 · see citing works

xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2012869 · Cited by 537 · see citing works

Multimodal Convolutional Neural Networks for Matching Image and Sentence

Ma, L., Lu, Z., Shang, L., & Li, H. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.301 · Cited by 334 · see citing works

Cortically coupled computer vision for rapid image search

Gerson, A. D., Parra, L. C., & Sajda, P. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875550 · Cited by 258 · see citing works

Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI

Manor, R., & Geva, A. B. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00146 · Cited by 144 · see citing works

A robust sensor-selection method for P300 brain–computer interfaces

Cecotti, H., Rivet, B., Congedo, M., Christian Jutten, B., Bertrand, O., Maby, E., & Mattout, J. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/1/016001 · Cited by 106 · see citing works

Foundations of Augmented Cognition

Schmorrow, D., & Fidopiastis, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39454-6 · Cited by 48 · see citing works

MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization

Mangin, O., Filliat, D., ten Bosch, L., & Oudeyer, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140732 · Cited by 21 · see citing works

Human-autonomy sensor fusion for rapid object detection

Robinson, R. M., Lee, H., McCourt, M. J., Marathe, A. R., Kwon, H., Ton, C. T., & Nothwang, W. D. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353390 · Cited by 18 · see citing works

A New Single Trial P300 Classification Method

Li, K., Sankar, R. T., Cao, K., Arbel, Y., & Donchin, E. (2012) · International Journal of E-Health and Medical Communications · doi:10.4018/jehmc.2012100103 · Cited by 4 · 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 · 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

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

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

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

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

Deep learning-based EEG emotion recognition: Current trends and future perspectives

Wang, X., Ren, Y., Luo, Z., He, W., Hong, J. S., & Huang, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1126994 · Cited by 161 · 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

Learning joint space–time–frequency features for EEG decoding on small labeled data.

Zhao, D., Tang, F., Si, B., & Feng, X. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.02.009 · Cited by 123 · see citing works

Deep learning for EEG data analytics: A survey

Li, G., Lee, C. H., Jung, J. J., Youn, Y. C., & Camacho, D. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5199 · Cited by 122 · 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 systematic comparison of deep learning methods for EEG time series analysis

Walther, D., Viehweg, J., Haueisen, J., & Mäder, P. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1067095 · Cited by 53 · see citing works

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Opałka, S., Stasiak, B., Szajerman, D., & Wojciechowski, A. (2018) · Sensors · doi:10.3390/s18103451 · Cited by 40 · see citing works

Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

Hartmann, K. G., Schirrmeister, R. T., & Ball, T. (2018) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2018.8311493 · Cited by 35 · see citing works

Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection

Mao, J., Qiu, S., Wei, W., & He, H. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.009 · Cited by 29 · see citing works

Automated visual stimuli evoked multi-channel EEG signal classification using EEGCapsNet

Kumari, N., Anwar, S., & Bhattacharjee, V. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.11.019 · Cited by 28 · see citing works

Decoding P300 Variability Using Convolutional Neural Networks

Solon, A. J., Lawhern, V. J., Touryan, J., McDaniel, J. R., Ries, A. J., & Gordon, S. M. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00201 · Cited by 28 · see citing works

DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets

Fan, L., Shen, H., Xie, F., Su, J., Yu, Y., & Hu, D. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3184725 · Cited by 27 · see citing works

SSVEP-assisted RSVP brain–computer interface paradigm for multi-target classification

Ko, L., Sankar, D. S. V., Huang, Y., Lu, Y.-C., Shaw, S., & Jung, T. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abd1c0 · Cited by 21 · see citing works

A review of feature extraction and classification algorithms for image RSVP-based BCI

Wang, Z., Healy, G. F., Smeaton, A. F., & Ward, T. E. (2018) · Institution of Engineering and Technology eBooks · doi:10.1049/pbce114e_ch12 · Cited by 21 · see citing works

DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing

Hakim, A., Golan, I., Yefet, S., & Levy, D. J. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1153413 · Cited by 19 · see citing works

Learning visual stimulus-evoked EEG manifold for neural image classification

Falciglia, S., Betello, F., Russo, S., & Napoli, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127654 · Cited by 19 · see citing works

Deep Learning Methods for EEG Neural Classification

Nakagome, S., Craik, A., Ravindran, A. S., He, Y., Cruz-Garza, J. G., & Contreras-Vidal, J. L. (2022) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_78-1 · Cited by 12 · see citing works

Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals

Guo, Y., Gok, S., & Şahin, M. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00689 · Cited by 11 · see citing works

Feature Selection of Deep Learning Models for EEG-Based RSVP Target Detection

Chen, J., Mao, Z., Zheng, R., Huang, Y., & He, L. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edp7095 · Cited by 11 · see citing works

Convolutional Neural Network-Based Visually Evoked EEG Classification Model on MindBigData

Kumari, N., Anwar, S., & Bhattacharjee, V. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-1543-6_22 · Cited by 7 · see citing works

Deep Learning Methods for EEG Neural Classification

Nakagome, S., Craik, A., Ravindran, A. S., He, Y., Cruz-Garza, J. G., & Contreras-Vidal, J. L. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_78 · Cited by 6 · see citing works

Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain–Computer Interface Application

Chiou, N., Günal, M., Koyejo, S., Perpetuini, D., Chiarelli, A. M., Low, K. A., Fabiani, M., & Gratton, G. (2024) · Bioengineering · doi:10.3390/bioengineering11080781 · Cited by 4 · see citing works

Semantic image sorting method for RSVP presentation

Wu, H., Li, F., Chu, W., Li, Y., Niu, Y., Shi, G., Zhang, L., & Chen, Y. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad4593 · Cited by 4 · see citing works

Convolutional Networks Outperform Linear Decoders in Predicting EMG from Spinal Cord Signals

Guo, Y. P., Gok, S., & Şahin, M. (2020) · Vide Leaf, Hyderabad eBooks · doi:10.37247/pans.1.2020.15 · Cited by 3 · see citing works

Exploiting Simplified Depth Estimation for Stereo-based 2D Object Detection

Lee, Y., Lee, H., Lee, E., Kwon, H., & Bhattacharyya, S. (2022) · International Conference on Artificial Intelligence and Pattern Recognition · doi:10.1109/aipr57179.2022.10092234 · Cited by 1 · see citing works

Practical electroencephalography (EEG) applications in stroke rehabilitation: Towards brain-computer interface (BCI) setup and motor function assessment

Zhang, X. (2019) · Summit (Simon Fraser University) · Cited by 1 · see citing works

Classifying single-trial event-related potentials using xDAWN and denoising auto-encoder

Cao, S., Wu, W., & Qi, F. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8028248

BFRCNet: addressing the class imbalance problem in the rapid serial visual presentation paradigm for decoding

Xu, M., Gao, X., Li, F., Zhang, B., & Chen, Y. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae2d9a

Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI

Manor, R., & Geva, A. B. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00146 · Cited by 144 · 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 · see citing works

Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification

Roots, K., Muhammad, Y., & Muhammad, N. (2020) · Computers · doi:10.3390/computers9030072 · Cited by 89 · see citing works

Bi-dimensional representation of EEGs for BCI classification using CNN architectures

Hernandez-Gonzalez, E., Gomez-Gil, P., Bojorges-Valdez, E., & Ramírez-Cortés, J. M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629958 · Cited by 11 · see citing works

Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion

Amin, S. U., Alsulaiman, M., Muhammad, G., Mekhtiche, M. A., & Shamim Hossain, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.027 · Cited by 523 · see citing works