Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Fifth Edition

ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

Mognon, A., Jovicich, J., Bruzzone, L., & Buiatti, M. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01061.x · Cited by 1,274 · see citing works

Endogenous electric fields may guide neocortical network activity

Fröhlich, F., & McCormick, D. A. (2010) · Neuron · doi:10.1016/j.neuron.2010.06.005 · Cited by 913 · see citing works

Removal of Artifacts from EEG Signals: A Review

Xiao, J., Bian, G., & Tian, Z. (2019) · Sensors · doi:10.3390/s19050987 · Cited by 842 · see citing works

Research Review for Broad Learning System: Algorithms, Theory, and Applications

Gong, X., Zhang, T., Chen, C. L. P., & Liu, Z. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3061094 · Cited by 354 · see citing works

Soft Materials in Neuroengineering for Hard Problems in Neuroscience

Jeong, J.-W., Shin, G., Park, S. I., Yu, K. J., Xu, L., & Rogers, J. A. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.035 · Cited by 333 · 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

A deep CNN approach to decode motor preparation of upper limbs from time–frequency maps of EEG signals at source level

Mammone, N., Ieracitano, C., & Morabito, F. C. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.01.027 · Cited by 156 · see citing works

A Multi-Scale Fusion Convolutional Neural Network Based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding

Li, D., Xu, J., Wang, J., Fang, X., & Ji, Y. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3037326 · Cited by 132 · see citing works

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Lima, A. A., Mridha, M. F., Das, S. C., Kabir, M. M., Islam, M. R., & Watanobe, Y. (2022) · Biology · doi:10.3390/biology11030469 · Cited by 132 · see citing works

Connectomics of human electrophysiology

Sadaghiani, S., Brookes, M. J., & Baillet, S. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118788 · Cited by 113 · see citing works

Efficacy of transformer networks for classification of EEG data

Siddhad, G., Gupta, A., Dogra, D. P., & Roy, P. P. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105488 · Cited by 86 · see citing works

Pinging the brain with transcranial magnetic stimulation reveals cortical reactivity in time and space

Ahn, S., & Frӧhlich, F. (2021) · Brain stimulation · doi:10.1016/j.brs.2021.01.018 · Cited by 82 · see citing works

Effects of thermal-olfactory interactions on emotional changes in urban outdoor environments

Chang, J., Du, M., Hong, B., Qu, H., & Chen, H. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110049 · Cited by 75 · see citing works

Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

Pineda, A. M., Ramos, F. M., Betting, L. E., & Campanharo, A. S. L. O. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231169 · Cited by 71 · see citing works

Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review

Oung, Q. W., Muthusamy, H., Lee, H. L., Basah, S. N., Yaacob, S., Sarillee, M., & Lee, C. K. (2015) · Sensors · doi:10.3390/s150921710 · Cited by 70 · see citing works

Instructor–learner neural synchronization during elaborated feedback predicts learning transfer.

Zhu, Y., Leong, V., Hou, Y., Zhang, D., Pan, Y., & Hu, Y. (2021) · Journal of Educational Psychology · doi:10.1037/edu0000707 · Cited by 70 · see citing works

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network

Wang, Y., Lin, K., Qi, Y., Lian, Q., Feng, S., Wu, Z., & Pan, G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2842769 · Cited by 69 · see citing works

Unifying turbulent dynamics framework distinguishes different brain states

Escrichs, A., Perl, Y. S., Uribe, C., Càmara, E., Türker, B., Pyatigorskaya, N., López‐González, A., Pallavicini, C., Panda, R., Annen, J., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03576-6 · Cited by 65 · see citing works

A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction

Zhang, J., Zheng, S., Chen, W., Du, G., Fu, Q., & Jiang, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-67855-4 · Cited by 64 · see citing works

EEG signal analysis using deep learning: A systematic literature review

Amrani, G., Adadi, A., Berrada, M., Souirti, Z., & Boujraf, S. (2021) · International Conference on Intelligent Computing in Data Sciences (ICDS) · doi:10.1109/icds53782.2021.9626707 · Cited by 57 · see citing works

LSTM-CNN model of drowsiness detection from multiple consciousness states acquired by EEG

Lee, C., & An, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119032 · Cited by 56 · see citing works

Long‐range temporal correlations in the subthalamic nucleus of patients with Parkinson’s disease

Hohlefeld, F. U., Huebl, J., Huchzermeyer, C., Schneider, G., Schönecker, T., Kühn, A. A., Curio, G., & Nikulin, V. V. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08198.x · Cited by 54 · see citing works

Elimination of Ocular Artifacts From Single Channel EEG Signals Using FBSE-EWT Based Rhythms

Gajbhiye, P., Tripathy, R. K., & Pachori, R. B. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2959697 · Cited by 54 · see citing works

Quantification of dynamic cerebral autoregulation: welcome to the jungle!

Brassard, P., Roy, M., Burma, J. S., Labrecque, L., & Smirl, J. D. (2023) · Clinical Autonomic Research · doi:10.1007/s10286-023-00986-2 · Cited by 50 · see citing works

EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism

Zhang, X., Li, J., Hou, K., Hu, B., Shen, J. G., Pan, J., & Hu, B. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175956 · Cited by 50 · see citing works

Ictal localization by source analysis of infraslow activity in DC-coupled scalp EEG recordings

Miller, J. W., Kim, W., Holmes, M., & Vanhatalo, S. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.12.018 · Cited by 47 · see citing works

Deep Learning Algorithms in EEG Signal Decoding Application: A Review

Vallabhaneni, R. B., Sharma, P., Kumar, V., Kulshreshtha, V., Reddy, K. J., Kumar, S. S., Kumar, V. S., & Bitra, S. K. (2021) · IEEE Access · doi:10.1109/access.2021.3105917 · Cited by 46 · see citing works

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Mahmud, S., Hossain, M. S., Chowdhury, M. E. H., & Reaz, M. B. I. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08111-6 · Cited by 39 · see citing works

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

Schmidt, D., Villalba-Díez, J., Ordieres-Meré, J. B., Gevers, R., Schwiep, J., & Molina, M. (2020) · Sensors · doi:10.3390/s20102860 · Cited by 39 · see citing works

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Qian, X., Qiu, Y., He, Q., Lu, Y., Lin, H. T., Xu, F., Zhu, F., Liu, Z., Li, X., Cao, Y., et al. (2021) · Brain Sciences · doi:10.3390/brainsci11101274 · Cited by 39 · see citing works

The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects

Peng, Y. L., Xu, Q., Lin, S., Wang, X., Xiang, G., Huang, S., Zhang, H., & Fan, C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.919695 · Cited by 39 · see citing works

Real-time seizure prediction from local field potentials using an adaptive Wiener algorithm

Rajdev, P., Ward, M. P., Rickus, J. L., Worth, R. M., & Irazoqui, P. P. (2009) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2009.11.006 · Cited by 38 · see citing works

DM-RE2I: A framework based on diffusion model for the reconstruction from EEG to image

Zeng, H., Xia, N., Qian, D., Hattori, M., Wang, C., & Kong, W. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105125 · Cited by 37 · see citing works

CNN models for EEG motor imagery signal classification

Alnaanah, M. A., Wahdow, M., & Alrashdan, M. H. S. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02293-1 · Cited by 37 · see citing works

Zeffiro User Interface for Electromagnetic Brain Imaging: a GPU Accelerated FEM Tool for Forward and Inverse Computations in Matlab

He, Q., Rezaei, A., & Pursiainen, S. (2018) · Neuroinformatics · doi:10.1007/s12021-019-09436-9 · Cited by 42 · see citing works

Automatic classification of sleep stages using EEG signals and convolutional neural networks

Masad, I. S., Alqudah, A., & Qazan, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0297582 · Cited by 34 · see citing works

Technologies for non-invasive physiological sensing: Status, challenges, and future horizons

Yu, Y., Jain, B., Anand, G., Heidarian, M., Lowe, A., & Kalra, A. M. (2023) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2023.100420 · Cited by 34 · see citing works

Forward simulation and inverse dipole localization with the lowest order Raviart—Thomas elements for electroencephalography

Pursiainen, S., Sorrentino, A., Campi, C., & Piana, M. (2011) · Inverse Problems · doi:10.1088/0266-5611/27/4/045003 · Cited by 34 · see citing works

Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis

Hossain, M. S., Reaz, M. B. I., Chowdhury, M. E. H., Ali, S. H. M., Bakar, A. A. A., Serkan Kiranyaz, B., Khandakar, A. A., Alhatou, M. I., & Habib, R. (2022) · IEEE Access · doi:10.1109/access.2022.3159155 · Cited by 33 · see citing works

Cardiac Artifact Noise Removal From Sleep EEG Signals Using Hybrid Denoising Model

Ranjan, R., Sahana, B. C., & Bhandari, A. K. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3198441 · Cited by 33 · see citing works

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance

Tsai, B.-Y., Diddi, S. V. S., Ko, L., Wang, S., Chang, C.-Y., & Jung, T. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3174528 · Cited by 32 · see citing works

Enhancement for P300-speller classification using multi-window discriminative canonical pattern matching

Xiao, X., Xu, M., Han, J. H., Yin, E., Liu, S., Zhang, X., Jung, T., & Ming, D. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac028b · Cited by 31 · see citing works

A practical review of electroencephalography’s value to consumer research

McInnes, A. N., Sung, B., & Hooshmand, R. (2022) · International Journal of Market Research · doi:10.1177/14707853221112622 · Cited by 31 · see citing works

A Method for Optimizing the Artifact Subspace Reconstruction Performance in Low-Density EEG

Cataldo, A., Criscuolo, S., Benedetto, E. D., Masciullo, A., Pesola, M., Schiavoni, R., & Invitto, S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3208768 · Cited by 31 · see citing works

Epileptic EEG Classification via Graph Transformer Network

Lian, J., & Xu, F. (2023) · International Journal of Neural Systems · doi:10.1142/s0129065723500429 · Cited by 31 · see citing works

EEG-CogNet: A deep learning framework for cognitive state assessment using EEG brain connectivity

Panwar, N., Pandey, V. K., & Roy, P. P. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106770 · Cited by 30 · see citing works

Exploring EEG Spectral Patterns in Episodic and Chronic Migraine During the Interictal State: Determining Frequencies of Interest in the Resting State

Gómez‐Pilar, J., García‐Azorín, D., Gomez-Lopez-de-San-Roman, C., Peral, Á. L. G., & Hornero, R. (2020) · Pain Medicine · doi:10.1093/pm/pnaa117 · Cited by 30 · see citing works

Contrasting Electroencephalography-Derived Entropy and Neural Oscillations With Highly Skilled Meditators

Young, J. H., Arterberry, M. E., & Martin, J. P. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.628417 · Cited by 28 · see citing works

Cross-Channel Specific-Mutual Feature Transfer Learning for Motor Imagery EEG Signals Decoding

Li, D., Wang, J., Xu, J., Fang, X., & Ji, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3269512 · Cited by 27 · see citing works

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

Hossain, M. S., Chowdhury, M. E. H., Reaz, M. B. I., Ali, S. H. M., Bakar, A. A. A., Serkan Kiranyaz, B., Khandakar, A. A., Alhatou, M. I., Habib, R., & Hossain, M. (2022) · Sensors · doi:10.3390/s22093169 · Cited by 27 · see citing works

Restoration of motion-corrupted EEG signals using attention-guided operational CycleGAN

Mahmud, S., Chowdhury, M. E. H., Serkan Kiranyaz, B., Emadi, N. A., Tahir, A., Hossain, M. S., Khandakar, A. A., & Al-Maadeed, S. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107514 · Cited by 27 · see citing works

EEG emotion recognition based on Ordinary Differential Equation Graph Convolutional Networks and Dynamic Time Wrapping

Chen, Y., Xu, X., Bian, X., & Qin, X. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111181 · Cited by 26 · see citing works

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network

Cai, Y., Zhao, M., & Huang, D. (2025) · Sensors · doi:10.3390/s25010231 · Cited by 26 · see citing works

Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace Techniques

Kumar, M. G., Narayanan, S., Sur, M., & Murthy, H. A. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3067998 · Cited by 26 · see citing works

Real-time noise cancellation with deep learning

Porr, B., Daryanavard, S., Bohollo, L. M., Cowan, H., & Dahiya, R. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277974 · Cited by 25 · see citing works

Electroencephalography and Mild Traumatic Brain Injury

Thatcher, R. W. (2006) · Springer eBooks · doi:10.1007/0-387-32565-4_11 · Cited by 24 · see citing works

Applied Affective Computing

Tian, L., Oviatt, S. L., Muszyński, M., Chamberlain, B. C., Healey, J., & Sano, A. (2022) · ACM eBooks · doi:10.1145/3502398 · Cited by 23 · see citing works

Evidence of Task-Independent Person-Specific Signatures in EEG Using Subspace TechniquesPreprint

Kumar, M. G., Narayanan, S. S., Sur, M., & Murthy, H. A. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 23 · see citing works

Altered directional functional connectivity underlies post-stroke cognitive recovery

Soleimani, B., Dallasta, I., Das, P., Kulasingham, J. P., Girgenti, S. G., Simon, J. Z., Babadi, B., & Marsh, E. B. (2023) · Brain Communications · doi:10.1093/braincomms/fcad149 · Cited by 22 · see citing works

An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals

Nayak, A. B., Shah, A., Maheshwari, S., Anand, V., Chakraborty, S., & Kumar, T. S. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100420 · Cited by 22 · see citing works

Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems

McCann, H. M., & Beltrachini, L. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac43f7 · 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

Effects of tactile perceptions on elderly adults’ thermo-physiological responses in outdoor open spaces

Du, M., Zheng, Y., Wang, C., Wang, Z., & Hong, B. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112958 · Cited by 20 · see citing works

Conductive Hydrogel‐Enabled Electrode for Scalp Electroencephalography Monitoring

Ji, Z., Li, L., Zheng, M., Ye, X., Yan, W., Wang, Z., Liu, Y., Wang, Y., Zhang, Y., Zhou, P., et al. (2025) · Small Methods · doi:10.1002/smtd.202501242 · Cited by 20 · see citing works

Use of portable EEG sensors to detect meditation

Anwar, D. N., Garg, P., Naik, V. S., Gupta, A., & Kumar, A. (2018) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets.2018.8328299 · Cited by 19 · see citing works

Automatic Detection of Mental Health Status using Alpha Subband of EEG Data

Ranjan, R., Neeti, & Sahana, B. C. (2022) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea54994.2022.9856586 · Cited by 19 · see citing works

Dynamical Models in Neuroscience From a Closed-Loop Control Perspective

Martínez, S., García-Violini, D., Belluscio, M., Piriz, J., & Sánchez‐Peña, R. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3180559 · Cited by 19 · see citing works

Electrophysiological predictors of clinical response to antidepressants

Bruder, G. E., Tenke, C. E., & Jürgen Kayser, A. (2013) · Cambridge University Press eBooks · doi:10.1017/cbo9781139175869.030 · Cited by 19 · see citing works

Meta-Learning for Fast and Privacy-Preserving Source Knowledge Transfer of EEG-Based BCIs

Li, S., Wu, H., Ding, L., & Wu, D. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2022.3199622 · Cited by 17 · see citing works

Brainsourcing: Crowdsourcing Recognition Tasks via Collaborative Brain-Computer Interfacing

Davis, K. M., Kangassalo, L., Spapé, M., & Ruotsalo, T. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376288 · Cited by 17 · see citing works

Multisensory interactions in urban parks: Auditory-olfactory effects on psycho-physiological responses of hypertensive elderly adults

Li, J., Ma, X., Wang, C., Li, X., Peng, Z., & Hong, B. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.113413 · Cited by 16 · see citing works

E2SGAN: EEG-to-SEEG translation with generative adversarial networks

Hu, M., Chen, J., Jiang, S., Ji, W., Mei, S., Chen, L., & Wang, X. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.971829 · Cited by 16 · see citing works

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

Wang, C., Zhu, X.-R., Zhou, X., Li, J., Lan, L., Huang, D., Zheng, Y., & Cai, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3264521 · Cited by 16 · see citing works

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Alahmadi, T. J., Rahman, A. U., Alhababi, Z. A., Ali, S., & Alkahtani, H. K. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad38fe · Cited by 15 · see citing works

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Huang, X., Liang, S., Li, Z., Lai, C. Y. Y., & Choi, K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269001 · Cited by 15 · see citing works

Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure

de Matos, J. C. R. G., Peralta, G., Heyse, J., Ménétré, E., Seeck, M., & van Mierlo, P. (2022) · Bioengineering · doi:10.3390/bioengineering9110690 · Cited by 13 · see citing works

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Jiao, M., Xian, X., Wang, B., Zhang, Y., Yang, S., Chen, S. C., Sun, H., & Liu, F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120802 · Cited by 13 · see citing works

Epilepsy EEG Seizure Prediction Based on the Combination of Graph Convolutional Neural Network Combined with Long- and Short-Term Memory Cell Network

Kuang, Z., Liu, S., Zhao, J. M., Wang, L., & Li, Y. (2024) · Applied Sciences · doi:10.3390/app142411569 · Cited by 13 · see citing works

Pain classification using functional near infrared spectroscopy and assessment of virtual reality effects in cancer pain management

Shafiei, S. B., Shadpour, S., Pangburn, B., Bentley-McLachlan, M., & de León-Casasola, O. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-93678-y · Cited by 12 · see citing works

Low Dimensional EEG Classification for Alzheimer’s Disease Recognition

Nedeljković, M., Pađin, P., & Nikolić, S. (2023) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor59449.2023.10372658 · Cited by 11 · see citing works

Modeling Visual Fatigue in Remote Tower Air Traffic Controllers: A Multimodal Physiological Data-Based Approach

Liang, R., Pan, W., Zuo, Q., Zhang, C. G., Chen, S., Chen, S., & Deng, L. (2025) · Aerospace · doi:10.3390/aerospace12060474 · Cited by 11 · see citing works

I think I like this: assessing conscious versus subconscious wine taste responses using neuroscientific techniques

Robertson, J., Ferreira, C. C., Reyneke, M., & Rosenstein, D. (2021) · International Journal of Wine Business Research · doi:10.1108/ijwbr-09-2020-0047 · Cited by 11 · see citing works

Statistical models of complex brain networks: a maximum entropy approach

Dichio, V., & Fallani, F. D. V. (2023) · Reports on Progress in Physics · doi:10.1088/1361-6633/ace6bc · Cited by 11 · see citing works

Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network — Gradient Penalty Model

Leng, J., Zhu, J., Yan, Y., Yu, X., Liu, M., Lou, Y., Liu, Y., Gao, L., Sun, Y., He, T., et al. (2023) · International Journal of Neural Systems · doi:10.1142/s0129065723500673 · Cited by 11 · see citing works

Using Electroencephalography (EEG) Power Responses to Investigate the Effects of Ambient Oxygen Content, Safety Shoe Type, and Lifting Frequency on the Worker’s Activities

Ramadan, M. Z., Ghaleb, A. M., & Ragab, A. E. (2020) · BioMed Research International · doi:10.1155/2020/7956037 · Cited by 11 · see citing works

SSVEP-Based Brain-Computer Interface for Part-Picking Robotic Co-Worker

Li, Y., & Kesavadas, T. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4051596 · Cited by 10 · see citing works

Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach

Cho, J. E., Kang, S. Y., Hong, Y. Y., & Jun, H. J. (2025) · Applied Sciences · doi:10.3390/app15084217 · Cited by 10 · see citing works

Insomnia and emotional dysfunction: Altered brain network connectivity across sleep and wakefulness states

Li, S., Wang, Z., Li, Y., Luo, X., Ru, T., Chen, Q., & Zhou, G. (2025) · Sleep Medicine · doi:10.1016/j.sleep.2025.106582 · Cited by 9 · see citing works

Multi frequency band fusion method for EEG signal classification

Wahdow, M., Alnaanah, M. A., Fadel, W., Adolf, A., Köllőd, C. M., & Ulbert, I. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02399-6 · Cited by 9 · see citing works

A neglected consumer neuroscience technique: Pupillometry and its practical application to consumer research

McInnes, A. N., & Sung, B. (2024) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.11.005 · Cited by 8 · see citing works

A Bi-LSTM Based Network with Attention Mechanism for EEG Visual Classification

Mo, L., Wang, Y., Zhou, W., Shen, X., & Kong, W. (2021) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus52573.2021.9641318 · Cited by 8 · see citing works

Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals

Criscuolo, S., Apicella, A., Prevete, R., & Longo, L. (2024) · Computer Standards & Interfaces · doi:10.1016/j.csi.2024.103897 · Cited by 8 · see citing works

Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)

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EEG personal recognition based on ‘qualified majority’ over signal patches

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