Analyzing Neural Time Series Data: Theory and Practice

DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG

Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2721116 · Cited by 1,376 · see citing works

DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices

Katsigiannis, S., & Ramzan, N. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2688239 · Cited by 1,135 · see citing works

Granger causality analysis in neuroscience and neuroimaging

Seth, A. K., Barrett, A. B., & Barnett, L. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4399-14.2015 · Cited by 928 · see citing works

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data

Grootswagers, T., Wardle, S. G., & Carlson, T. A. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01068 · Cited by 712 · see citing works

Where Does EEG Come From and What Does It Mean?

Cohen, M. X. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.02.004 · Cited by 722 · see citing works

Nasal Respiration Entrains Human Limbic Oscillations and Modulates Cognitive Function

Zelano, C., Jiang, H., Zhou, G., Arora, N., Schuele, S., Rosenow, J., & Gottfried, J. A. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2586-16.2016 · Cited by 599 · see citing works

Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior

Cohen, M. X., & Donner, T. H. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00479.2013 · Cited by 494 · see citing works

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

Mousavi, S., Afghah, F., & Acharya, U. R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216456 · Cited by 536 · see citing works

A better way to define and describe Morlet wavelets for time-frequency analysis

Cohen, M. X. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.048 · Cited by 501 · see citing works

Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

Purcell, S. M., Manoach, D. S., Demanuele, C., Cade, B. E., Mariani, S., Cox, R., Panagiotaropoulou, G., Saxena, R., Pan, J. Q., Smoller, J. W., et al. (2017) · Nature Communications · doi:10.1038/ncomms15930 · Cited by 439 · see citing works

The threshold for conscious report: Signal loss and response bias in visual and frontal cortex

van Vugt, B., Dagnino, B., Vartak, D., Safaai, H., Panzeri, S., Dehaene, S., & Roelfsema, P. R. (2018) · Science · doi:10.1126/science.aar7186 · Cited by 394 · see citing works

Layer-specific modulation of neocortical dendritic inhibition during active wakefulness

Muñoz, W., Tremblay, R., Levenstein, D., & Rudy, B. (2017) · Science · doi:10.1126/science.aag2599 · Cited by 305 · see citing works

Wearable EEG and beyond

Casson, A. J. (2019) · Biomedical Engineering Letters · doi:10.1007/s13534-018-00093-6 · Cited by 345 · see citing works

Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders

Tsinalis, O., Matthews, P. M., & Guo, Y. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-015-1444-y · Cited by 322 · see citing works

Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia

Schartner, M., Seth, A., Noirhomme, Q., Boly, M., Bruno, M.-A., Laureys, S., & Barrett, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0133532 · Cited by 287 · see citing works

Methodological considerations for studying neural oscillations

Donoghue, T., Schaworonkow, N., & Voytek, B. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15361 · Cited by 309 · see citing works

Mixed Neural Network Approach for Temporal Sleep Stage Classification

Dong, H., Supratak, A., Pan, W., Wu, C., Matthews, P. M., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2733220 · Cited by 294 · see citing works

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials

Waytowich, N., Lawhern, V. J., Garcia, J. O., Cummings, J., Faller, J., Sajda, P., & Vettel, J. M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae5d8 · Cited by 262 · 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

Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

Baniqued, P. D. E., Stanyer, E. C., Awais, M., Alazmani, A., Jackson, A. E., Mon-Williams, M. A., Mushtaq, F., & Holt, R. J. (2021) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-021-00820-8 · Cited by 250 · see citing works

Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research

Pernet, C. R., Garrido, M. I., Gramfort, A., Maurits, N. M., Michel, C. M., Pang, E. W., Salmelin, R. H., Schoffelen, J., Valdes-Sosa, P. A., & Puce, A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00709-0 · Cited by 245 · see citing works

Geometry of sequence working memory in macaque prefrontal cortex

Xie, Y., Hu, P., Li, J., Chen, J., Song, W., Wang, X., Yang, T., Dehaene, S., Tang, S., Min, B., et al. (2022) · Science · doi:10.1126/science.abm0204 · Cited by 215 · see citing works

The Maryland analysis of developmental EEG (MADE) pipeline

Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020) · Psychophysiology · doi:10.1111/psyp.13580 · Cited by 217 · see citing works

Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks

Lee, W., Kim, K., Park, J., Kim, J.-H., & Kim, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2883330 · Cited by 203 · see citing works

Quantification of Phase-Amplitude Coupling in Neuronal Oscillations: Comparison of Phase-Locking Value, Mean Vector Length, Modulation Index, and Generalized-Linear-Modeling-Cross-Frequency-Coupling

Hülsemann, M., nat, D. R., Naumann, E., nat, D. R., & Rasch, B. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00573 · Cited by 205 · see citing works

Neuronal dynamics direct cerebrospinal fluid perfusion and brain clearance

Jiang-Xie, L.-F., Drieu, A., Bhasiin, K., Quintero, D., Smirnov, I., & Kipnis, J. (2024) · Nature · doi:10.1038/s41586-024-07108-6 · Cited by 220 · see citing works

Consciousness among delta waves: a paradox?

Frohlich, J., Toker, D., & Monti, M. M. (2021) · Brain · doi:10.1093/brain/awab095 · Cited by 197 · see citing works

The neural dynamics of hierarchical Bayesian causal inference in multisensory perception

Rohe, T., Ehlis, A.-C., & Noppeney, U. (2019) · Nature Communications · doi:10.1038/s41467-019-09664-2 · Cited by 187 · see citing works

Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses

Vahid, A., Mückschel, M., Neuhaus, A. H., Stock, A., & Beste, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-34727-7 · Cited by 186 · see citing works

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood

Miskovic, V., Ma, X., Chou, C.-A., Fan, M., Owens, M., Sayama, H., & Gibb, B. E. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.06.013 · Cited by 175 · see citing works

ZapLine: A simple and effective method to remove power line artifacts

Alain De Cheveigné, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116356 · Cited by 188 · see citing works

Anticipatory Distractor Suppression Elicited by Statistical Regularities in Visual Search

Wang, B., van Driel, J., Ort, E., & Theeuwes, J. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01433 · Cited by 166 · see citing works

Macro and micro sleep architecture and cognitive performance in older adults

Djonlagic, I. E., Mariani, S., Fitzpatrick, A. L., van der Klei, V. M. G. T. H., Johnson, D. A., Wood, A. C., Seeman, T. E., Nguyen, H. T., Prerau, M. J., Luchsinger, J. A., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00964-y · Cited by 173 · see citing works

Control of working memory by phase–amplitude coupling of human hippocampal neurons

Daume, J., Kamiński, J., Gómez-Palacio-Schjetnan, A., Salimpour, Y., Khan, U., Kyzar, M., Reed, C. M., Anderson, W. S., Valiante, T. A., Mamelak, A. N., et al. (2024) · Nature · doi:10.1038/s41586-024-07309-z · Cited by 182 · see citing works

Best Practices for Event-Related Potential Research in Clinical Populations

Kappenman, E. S., & Luck, S. J. (2015) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.11.007 · Cited by 158 · see citing works

Reset of hippocampal–prefrontal circuitry facilitates learning

Park, A. J., Harris, A. Z., Martyniuk, K. M., Chang, C., Abbas, A. I., Lowes, D. C., Kellendonk, C., Gogos, J. A., & Gordon, J. A. (2021) · Nature · doi:10.1038/s41586-021-03272-1 · Cited by 158 · see citing works

Validating theta power as an objective measure of cognitive load in educational video

Castro-Meneses, L. J., Kruger, J., & Doherty, S. (2019) · Educational Technology Research and Development · doi:10.1007/s11423-019-09681-4 · Cited by 156 · see citing works

Transduction of the Geomagnetic Field as Evidenced from alpha-Band Activity in the Human Brain

Wang, C. X., Hilburn, I. A., Wu, D.-A. J., Mizuhara, Y., Cousté, C. P., Abrahams, J. N. H., Bernstein, S. E., Matani, A., Shinsuke Shimojo, A., & Kirschvink, J. L. (2019) · eNeuro · doi:10.1523/eneuro.0483-18.2019 · Cited by 154 · see citing works

PGCN: Pyramidal Graph Convolutional Network for EEG Emotion Recognition

Jin, M., Du, C., He, H., Cai, T., & Li, J. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3385676 · Cited by 154 · see citing works

A Graph Theory-Based Modeling of Functional Brain Connectivity Based on EEG: A Systematic Review in the Context of Neuroergonomics

Ismail, L. E., & Karwowski, W. (2020) · IEEE Access · doi:10.1109/access.2020.3018995 · Cited by 150 · see citing works

Decoding and Reconstructing the Focus of Spatial Attention from the Topography of Alpha-band Oscillations

Samaha, J., Sprague, T. C., & Postle, B. R. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00955 · Cited by 146 · see citing works

A review of rapid serial visual presentation-based brain–computer interfaces

Lees, S., Dayan, N., Cecotti, H., McCullagh, P., Maguire, L., Lotte, F., & Coyle, D. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa9817 · Cited by 149 · see citing works

Acetylcholine Release in Prefrontal Cortex Promotes Gamma Oscillations and Theta–Gamma Coupling during Cue Detection

Howe, W. M., Gritton, H. J., Lusk, N. A., Roberts, E. A., Hetrick, V. L., Berke, J. D., & Sarter, M. F. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2737-16.2017 · Cited by 147 · see citing works

The Caveats of observing Inter-Trial Phase-Coherence in Cognitive Neuroscience

van Diepen, R. M., & Mazaheri, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-20423-z · Cited by 147 · see citing works

Recommendations and publication guidelines for studies using frequency domain and time‐frequency domain analyses of neural time series

Keil, A., Bernat, E. M., Cohen, M. X., Ding, M., Fabiani, M., Gratton, G., Kappenman, E. S., Maris, E., Mathewson, K. E., Ward, R. T., et al. (2022) · Psychophysiology · doi:10.1111/psyp.14052 · Cited by 143 · see citing works

A human prefrontal-subthalamic circuit for cognitive control

Kelley, R. M., Flouty, O. E., Emmons, E. B., Kim, Y.-C., Kingyon, J. R., Wessel, J. R., Oya, H., Greenlee, J. D. W., & Narayanan, N. S. (2017) · Brain · doi:10.1093/brain/awx300 · Cited by 142 · see citing works

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Ismail, L. E., & Karwowski, W. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242857 · Cited by 153 · see citing works

Towards a systematization of brain oscillatory activity in actions

Beste, C., Münchau, A. M., & Frings, C. (2023) · Communications Biology · doi:10.1038/s42003-023-04531-9 · Cited by 144 · see citing works

Predicting task-general mind-wandering with EEG

Jin, C. Y., Borst, J. P., & van Vugt, M. K. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00707-1 · Cited by 136 · see citing works

High-pass filtering artifacts in multivariate classification of neural time series data

van Driel, J., Olivers, C. N. L., & Fahrenfort, J. J. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109080 · Cited by 135 · see citing works

Conscious processing of narrative stimuli synchronizes heart rate between individuals

Pérez, P., Madsen, J., Banellis, L., Türker, B., Raimondo, F., Perlbarg, V., Valente, M., Niérat, M.-C., Puybasset, L., Naccache, L., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109692 · Cited by 139 · see citing works

Propagation of hippocampal ripples to the neocortex by way of a subiculum-retrosplenial pathway

Nitzan, N., McKenzie, S., Beed, P. S., English, D. F., Oldani, S., Tukker, J. J., Buzsáki, G., & Schmitz, D. (2020) · Nature Communications · doi:10.1038/s41467-020-15787-8 · Cited by 129 · see citing works

Intraoperative Frontal Alpha-Band Power Correlates with Preoperative Neurocognitive Function in Older Adults

Giattino, C., Gardner, J. E., Sbahi, F. M., Roberts, K., Cooter, M. L., Moretti, E., Browndyke, J. N., Mathew, J. P., Woldorff, M. G., Berger, M., et al. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00024 · Cited by 129 · see citing works

Hippocampal pattern completion is linked to gamma power increases and alpha power decreases during recollection

Staresina, B. P., Michelmann, S., Bonnefond, M., Jensen, O. B., Axmacher, N., & Fell, J. (2016) · eLife · doi:10.7554/elife.17397 · Cited by 122 · see citing works

Alpha and theta band dynamics related to sentential constraint and word expectancy

Rommers, J., Dickson, D. S., Norton, J. J. S., Wlotko, E. W., & Federmeier, K. D. (2017) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2016.1183799 · Cited by 109 · see citing works

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates

Fiorillo, L., Favaro, P., & Faraci, F. D. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3117970 · Cited by 126 · see citing works

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities

Nikolaev, A. R., Meghanathan, R. N., & van Leeuwen, C. (2016) · Brain and Cognition · doi:10.1016/j.bandc.2016.06.004 · Cited by 120 · see citing works

What binds us? Inter-brain neural synchronization and its implications for theories of human consciousness

Valencia, A. L., & Froese, T. (2020) · Neuroscience of Consciousness · doi:10.1093/nc/niaa010 · Cited by 132 · see citing works

Conjunctive representations that integrate stimuli, responses, and rules are critical for action selection

Kikumoto, A., & Mayr, U. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1922166117 · Cited by 117 · see citing works

Distinct Oscillatory Frequencies Underlie Excitability of Human Occipital and Parietal Cortex

Samaha, J., Gosseries, O., & Postle, B. R. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3413-16.2017 · Cited by 113 · see citing works

Wearable, Integrated EEG–fNIRS Technologies: A Review

Uchitel, J., Vidal-Rosas, E. E., Cooper, R. J., et al. (2021) · Sensors · doi:10.3390/s21186106 · Cited by 111 · see citing works

Frontal theta and beta oscillations during lower-limb movement in Parkinson’s disease

Singh, A. P., Cole, R. C., Espinoza, A. I., Brown, D. R., Cavanagh, J. F., & Narayanan, N. S. (2020) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.12.399 · Cited by 108 · see citing works

Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control

Gulbinaite, R., van Rijn, H., & Cohen, M. X. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00761 · Cited by 109 · see citing works

GABAergic interneurons excite neonatal hippocampus in vivo

Murata, Y., & Colonnese, M. T. (2020) · Science Advances · doi:10.1126/sciadv.aba1430 · Cited by 101 · see citing works

Distinct mechanisms mediate speed-accuracy adjustments in cortico-subthalamic networks

Herz, D. M., Tan, H., Brittain, J., Fischer, P., Cheeran, B., Green, A. L., FitzGerald, J. J., Aziz, T. Z., Ashkan, K., Little, S. J., et al. (2017) · eLife · doi:10.7554/elife.21481 · Cited by 99 · see citing works

Stimulus-Driven Brain Rhythms within the Alpha Band: The Attentional-Modulation Conundrum

Keitel, C., Keitel, A., Benwell, C., Daube, C., Thut, G., & Groß, J. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1633-18.2019 · Cited by 103 · see citing works

The power of neural oscillations to inform sentence comprehension: A linguistic perspective

Prystauka, Y., & Lewis, A. (2019) · Language and Linguistics Compass · doi:10.1111/lnc3.12347 · Cited by 106 · see citing works

Posterior α EEG Dynamics Dissociate Current from Future Goals in Working Memory-Guided Visual Search

de Vries, I. E. J., van Driel, J., & Olivers, C. N. L. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2945-16.2016 · Cited by 106 · see citing works

Time-resolved parameterization of aperiodic and periodic brain activity

Wilson, L. E., Castanheira, J. D. S., & Baillet, S. (2022) · eLife · doi:10.7554/elife.77348 · Cited by 101 · see citing works

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

Cohen, M. X., & Gulbinaite, R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.036 · Cited by 103 · see citing works

Dynamic frontotemporal systems process space and time in working memory

Johnson, E. L., Adams, J. N., Solbakk, A., Endestad, T., Larsson, P. G., Ivanović, J., Meling, T. R., Lin, J. J., & Knight, R. T. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2004274 · Cited by 99 · see citing works

Cognitive processing of a common stimulus synchronizes brains, hearts, and eyes

Madsen, J., & Parra, L. C. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac020 · Cited by 91 · see citing works

A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior

Voloh, B., & Womelsdorf, T. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00018 · Cited by 96 · see citing works

Priority Switches in Visual Working Memory are Supported by Frontal Delta and Posterior Alpha Interactions

de Vries, I. E. J., van Driel, J., Karacaoglu, M., & Olivers, C. N. L. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy223 · Cited by 100 · see citing works

Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction

Özger, M., Başakın, E. E., Ekmekcioğlu, Ö., & Hacısüleyman, V. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105851 · Cited by 104 · see citing works

Frontal midline theta differentiates separate cognitive control strategies while still generalizing the need for cognitive control

Eisma, J. J., Rawls, E., Long, S. M., Mach, R. J., & Lamm, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-94162-z · Cited by 110 · see citing works

Multi-Modal Physiological Signals Based Squeeze-and-Excitation Network With Domain Adversarial Learning for Sleep Staging

Jia, Z., Cai, X., & Jiao, Z. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3140383 · Cited by 103 · see citing works

Window View and the Brain: Effects of Floor Level and Green Cover on the Alpha and Beta Rhythms in a Passive Exposure EEG Experiment

Olszewska-Guizzo, A., Escoffier, N., Chan, J., et al. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15112358 · Cited by 102 · see citing works

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

Rojas, R. F., Debie, E., Fidock, J. J. T., Barlow, M., Kasmarik, K., Anavatti, S. G., Garratt, M., & Abbass, H. A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00040 · Cited by 101 · see citing works

Stimulus-Induced Changes in 1/ f -like Background Activity in EEG

Gyurkovics, M., Clements, G. M., Low, K. A., Fabiani, M., & Gratton, G. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0414-22.2022 · Cited by 98 · see citing works

Attention differentially modulates the amplitude of resonance frequencies in the visual cortex

Gulbinaite, R., Roozendaal, D. H. M., & Rufin Vanrullen, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116146 · Cited by 93 · see citing works

Midfrontal neural dynamics distinguish between general control and inhibition-specific processes in the stopping of motor actions

Kaiser, J., Simon, N. A., Sauseng, P., & Schütz‐Bosbach, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-49476-4 · Cited by 99 · see citing works

Slow Oscillations during Sleep Coordinate Interregional Communication in Cortical Networks

Cox, R., van Driel, J., de Boer, M., & Talamini, L. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1953-14.2014 · Cited by 96 · see citing works

Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing

Jimenez-Molina, A., Retamal, C., & Lira, H. (2018) · Sensors · doi:10.3390/s18020458 · Cited by 95 · see citing works

Electrogastrography for psychophysiological research: Practical considerations, analysis pipeline, and normative data in a large sample

Wolpert, N., Rebollo, I., & Tallon‐Baudry, C. (2020) · Psychophysiology · doi:10.1111/psyp.13599 · Cited by 96 · see citing works

The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale

Gyurkovics, M., Clements, G. M., Low, K. A., Fabiani, M., & Gratton, G. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118192 · Cited by 94 · see citing works

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain–Computer Interface

Wei, T., Wang, Z., Wong, C. M., et al. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3342331 · Cited by 96 · see citing works

Memorability of words in arbitrary verbal associations modulates memory retrieval in the anterior temporal lobe

Xie, W., Bainbridge, W. A., Inati, S. K., Baker, C. I., & Zaghloul, K. A. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0901-2 · Cited by 87 · see citing works

Propofol-induced loss of consciousness is associated with a decrease in thalamocortical connectivity in humans

Malekmohammadi, M., Price, C. M., Hudson, A. E., Thum, J. A., & Pouratian, N. (2019) · Brain · doi:10.1093/brain/awz169 · Cited by 95 · see citing works

Using Muse: Rapid Mobile Assessment of Brain Performance

Krigolson, O., Hammerstrom, M. R., Abimbola, W., Trska, R., Wright, B., Hecker, K., & Binsted, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.634147 · Cited by 75 · see citing works

Timing variability and midfrontal ~4 Hz rhythms correlate with cognition in Parkinson’s disease

Singh, A. P., Cole, R. C., Espinoza, A. I., Evans, A., Cao, S., Cavanagh, J. F., & Narayanan, N. S. (2021) · npj Parkinson s Disease · doi:10.1038/s41531-021-00158-x · Cited by 90 · see citing works

SleepFCN: A Fully Convolutional Deep Learning Framework for Sleep Stage Classification Using Single-Channel Electroencephalograms

Goshtasbi, N., Boostani, R., & Sanei, S. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3192988 · Cited by 95 · see citing works

Assessing Task Mental Workload in Construction Projects: A Novel Electroencephalography Approach

Chen, J., Taylor, J., & Çomu, S. (2017) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001345 · Cited by 64 · see citing works

Precursor processes of human self-initiated action

Khalighinejad, N., Schurger, A., Desantis, A., Zmigrod, L., & Haggard, P. (2017) · bioRxiv · doi:10.1016/j.neuroimage.2017.09.057 · Cited by 79 · see citing works

Concurrent information affects response inhibition processes via the modulation of theta oscillations in cognitive control networks

Chmielewski, W. X., Mückschel, M., Dippel, G., & Beste, C. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1137-1 · Cited by 87 · see citing works

Rhythms in cognition: The evidence revisited

Keitel, C., Ruzzoli, M., Dugué, L., Busch, N. A., & Benwell, C. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15740 · Cited by 84 · see citing works

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Bridwell, D. A., Cavanagh, J. F., Collins, A. G. E., Nunez, M. D., Srinivasan, R., Stober, S., & Calhoun, V. D. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00106 · Cited by 84 · see citing works

Binding by random bursts: a computational model of cognitive control

Verguts, T. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01117 · Cited by 76 · see citing works

EEG dynamics reveal a dissociation between storage and selective attention within working memory

Günseli, E., Fahrenfort, J. J., van Moorselaar, D., Daoultzis, K., Meeter, M., & Olivers, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-49577-0 · Cited by 61 · see citing works

A roadmap for development of neuro-oscillations as translational biomarkers for treatment development in neuropsychopharmacology

Javitt, D. C., Siegel, S. J., Spencer, K., Mathalon, D. H., Hong, L. E., Martı́nez, A., Ehlers, C. L., Abbas, A. I., Teichert, T., Lakatos, P., et al. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0697-9 · Cited by 82 · see citing works

ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

Атабекова, Б. Б. (2024) · Habaršy. Pedagogika ġylymdary seriâsy · doi:10.51889/2959-5762.2023.80.4.019

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

ESKVS: efficient and secure approach for keyframes-based video summarization framework

Saini, P., & Berwal, K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18405-7 · Cited by 10 · see citing works