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

Speech recognition with deep recurrent neural networks

Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · see citing works

Brain–computer interfaces for communication and control

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927 · 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

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

Brain–Computer InterfacesPrinciples and Practice

Wolpaw, J. R., & Wolpaw, E. W. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388855.001.0001 · Cited by 1,114 · see citing works

Deep learning for healthcare applications based on physiological signals: A review

Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.005 · Cited by 1,042 · 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

Toward enhanced p300 speller performance

Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.07.017 · Cited by 749 · see citing works

Peripheral Neuron Survival and Outgrowth on Graphene

Convertino, D., Luin, S., Marchetti, L., & Coletti, C. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00001 · Cited by 516 · see citing works

Brain–Computer Interface Spellers: A Review

Rezeika, A., Benda, M., Stawicki, P., Gembler, F. W., Saboor, A., & Volosyak, I. (2018) · Brain Sciences · doi:10.3390/brainsci8040057 · Cited by 390 · see citing works

Deep learning based on Batch Normalization for P300 signal detection

Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., & Li, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.039 · Cited by 230 · 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

State-dependencies of learning across brain scales

Ritter, P., Jan Born, A., Brecht, M., Dinse, H. R., Heinemann, U., Pleger, B., Schmitz, D., Schreiber, S., Villringer, A., & Kempter, R. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00001 · Cited by 123 · see citing works

A feasibility clinical trial to improve social attention in autistic spectrum disorder (ASD) using a brain computer interface.

Amaral, C., Mouga, S., Simões, M., Pereira, H. C., Bernardino, I., Quental, H., Playle, R., McNamara, R., Oliveira, G., & Castelo-Branco, M. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00477 · Cited by 94 · see citing works

A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study

Amaral, C. P., Simões, M. A., Mouga, S., Andrade, J., & Castelo-Branco, M. S. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.07.029 · Cited by 86 · see citing works

Robust discriminative keyword spotting for emotionally colored spontaneous speech using bidirectional LSTM networks

Wöllmer, M., Eyben, F., Keshet, J., Graves, A., Schuller, B. W., & Rigoll, G. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4960492 · Cited by 66 · see citing works

P300-Based Asynchronous Brain Computer Interface for Environmental Control System

Aydın, E. A., Bay, Ö. F., & Güler, İ. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2690801 · Cited by 57 · see citing works

Towards an accessible use of smartphone-based social networks through brain-computer interfaces

Martínez-Cagigal, V., Santamaría-Vázquez, E., Gómez‐Pilar, J., & Hornero, R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.026 · Cited by 38 · see citing works

Self-calibration algorithm in an asynchronous P300-based brain–computer interface

Schettini, F., Fabio Aloise, A., Aricò, P., Salinari, S., Donatella Mattia, A., & Febo Cincotti, A. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/3/035004 · Cited by 34 · 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

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

Simões, M., Borra, D., Santamaría-Vázquez, E., GBT-UPM, Bittencourt-Villalpando, M., Krzemiński, D., Miladinović, A., Neural_Engineering_Group, Schmid, T., Zhao, H., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.568104 · Cited by 72 · see citing works

A recent advances on autism spectrum disorders in diagnosing based on machine learning and deep learning

Hatim, H. A., Alyasseri, Z. A. A., & Jamil, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11302-x · Cited by 46 · 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

CNN-FEBAC: A framework for attention measurement of autistic individuals

Patel, M., Bhatt, H., Munshi, M., Pandya, S., Jain, S., Thakkar, P., & Yoon, S. W. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105018 · Cited by 16 · see citing works

Can recurrent neural network enhanced EEGNet improve the accuracy of ERP classification task? An exploration and a discussion

Zhao, H., Yang, Y., Karlsson, P., & McEwan, A. L. (2020) · Health and Technology · doi:10.1007/s12553-020-00458-x · Cited by 15 · see citing works

A Deep Learning-Based Approach for Accurate Diagnosis of Alcohol Usage Severity Using EEG Signals

Kumari, N., Anwar, S., & Bhattacharjee, V. (2022) · IETE Journal of Research · doi:10.1080/03772063.2022.2038705 · Cited by 15 · see citing works

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

Liu, Y., Höllerer, T., & Sra, M. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.803384 · Cited by 6 · see citing works

Bridging Deep Learning and Quantum Computing for EEG-Driven P300 Targeted ASD Response Detection

Turjya, S. M., & Fawakherji, M. (2025) · IEEE Access · doi:10.1109/access.2025.3646589 · Cited by 5 · see citing works

The ensemble multi-scale convolution neural network for visual target detection EEG-based brain-computer interfaces

Wang, X., Dang, M., Yang, K., Cui, X., Zhang, D., & Chen, C. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106583 · Cited by 5 · see citing works

Enhancing ASD Cognitive Assessment with P300 Classification using EMD-based qEEG Wavelet Features

Das, M., & Mahadevappa, M. (2023) · International Conference on Computer Applications in Electrical Engineering-Recent Advances (CERA) · doi:10.1109/cera59325.2023.10455596 · Cited by 3 · see citing works

A Novel Neural Network-Based Approach to Classification of Implicit Emotional Components in Ordinary Speech

Shepelev, I. E., Бахтин, О. М., Lazurenko, D. M., Saevskiy, A., Shaposhnikov, D. G., & Kiroy, V. N. (2021) · Optical Memory and Neural Networks · doi:10.3103/s1060992x21010057 · Cited by 3 · see citing works

Stimulus-independent noninvasive BCI based on EEG patterns of inner speech

Kiroy, V. N., Aslanyan, E. V., Бахтин, О. М., Krivko, E., Paleev, A. V., Gerashchenko, A., Shevchenko, I. G., Saevskiy, A., & Shaposhnikov, D. G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95439-6.00011-9 · Cited by 1 · see citing works

An Optimal EEG Features Extraction Methods for Autism Detection

Hatim, H. A., & Alyasseri, Z. A. A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3942-7_47

Cross-Subject Event-Related Potential Classification via Multi-View Based Contrastive Learning

Chen, C., Xia, L., Zhuang, J., Qian, Q., Ji, H., & Li, J. (2026) · Brain Connectivity · doi:10.1177/21580014261462127

A Lightweight Multi-Timescale and Spatial Convolutional Skillful Cross-Stacking Model for Assisted Brain-Computer Interfaces

Zhang, J., & Haimudula, A. (2024) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce62493.2024.10653057

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces

Jin, J., Xu, R., Daly, I. P., Zhao, X., Wang, X., & Cichocki, A. S. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3390805 · Cited by 65 · 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

A Convolutional Neural Network Architecture for Decoding Visual Stimuli with EEGs

Zeller, K. D., Fabor, O. F., & Schlegel, D. R. (2018) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc45901.2018.9244790

A feasible classification algorithm for event-related potential (ERP) based brain-computer-interface (BCI) from IFMBE scientific challenge dataset

Zhao, H., Yu, S., Prinable, J., McEwan, A., & Karlsson, P. (2020) · IFMBE Proceedings · doi:10.1007/978-3-030-31635-8_226 · Cited by 9 · see citing works