Cited by 2,105EEG and Brain-Computer InterfacesNeural dynamics and brain functionUndergraduate Neuroscience Education and Research

The DOI matches its Crossref record.

Analyzing Neural Time Series Data: Theory and Practice

Cohen, Mike X·2014·The MIT Press eBooks

DOI 10.7551/mitpress/9609.001.0001

Open this paper in CoChat — free account

Abstract

A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.

Integrity signals

Registry

Crossref · 10.7551/mitpress/9609.001.0001

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 101–200 of 1,005

Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability
Maura, R. M., Parra, S. R., Stevens, R. E., Weeks, D. L., Wolbrecht, E. T., & Perry, J. C. (2023) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01142-7 · Cited by 87
Consumer Neuroscience: Attentional Preferences for Wine Labeling Reflected in the Posterior Contralateral Negativity
Alvino, L., Constantinides, E., & Lubbe, R. H. J. V. D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.688713 · Cited by 87
Comparison of linear spatial filters for identifying oscillatory activity in multichannel data
Cohen, M. X. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.12.016 · Cited by 79
Self-citation
Understanding the interplay of sleep and aging: Methodological challenges
Muehlroth, B. E., & Werkle‐Bergner, M. (2020) · Psychophysiology · doi:10.1111/psyp.13523 · Cited by 87
No Evidence that Predictions and Attention Modulate the First Feedforward Sweep of Cortical Information Processing
Alilović, J., Timmermans, B., Reteig, L. C., van Gaal, S., & Slagter, H. A. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz038 · Cited by 79
SleepPrintNet: A Multivariate Multimodal Neural Network Based on Physiological Time-Series for Automatic Sleep Staging
Jia, Z., Cai, X., Zheng, G., Wang, J., & Lin, Y. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3060350 · Cited by 86
Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach
Gregorio, F. D., Porta, F. L., Petrone, V., Battaglia, S., Orlandi, S., Ippolito, G., Romei, V., Piperno, R., & Lullini, G. (2022) · Biomedicines · doi:10.3390/biomedicines10081897 · Cited by 85
Detecting Unattended Stimuli Depends on the Phase of Prestimulus Neural Oscillations
Harris, A. M., Dux, P. E., & Mattingley, J. B. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3006-17.2018 · Cited by 77
Error, rather than its probability, elicits specific electrocortical signatures: a combined EEG-immersive virtual reality study of action observation
Pezzetta, R., Nicolardi, V., Tidoni, E., & Aglioti, S. M. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00130.2018 · Cited by 85
Estimating the Timing of Cognitive Operations With MEG/EEG Latency Measures: A Primer, a Brief Tutorial, and an Implementation of Various Methods
Liesefeld, H. R. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00765 · Cited by 79
MMCNN: A Multi-branch Multi-scale Convolutional Neural Network for Motor Imagery Classification
Jia, Z., Lin, Y., Wang, J., Yang, K., Liu, T., & Xinwang, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67664-3_44 · Cited by 85
Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks
López‐Madrona, V. J., Pérez-Montoyo, E., Álvarez-Salvado, E., Moratal, D., Herreras, Ó., Pereda, E., Mirasso, C. R., & Canals, S. (2020) · eLife · doi:10.7554/elife.57313 · Cited by 80
Theta‐ and delta‐band EEG network dynamics during a novelty oddball task
Harper, J., Malone, S. M., & Iacono, W. G. (2017) · Psychophysiology · doi:10.1111/psyp.12906 · Cited by 79
Multivariate cross-frequency coupling via generalized eigendecomposition
Cohen, M. X. (2017) · eLife · doi:10.7554/elife.21792 · Cited by 72
Self-citation
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
The reality of virtual reality
Schöne, B., Kisker, J., Lange, L., Gruber, T., Sylvester, S., & Osinsky, R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1093014 · Cited by 94
DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
Ávila, C. G., Bott, F. S., Tiemann, L., Hohn, V. D., May, E. S., Nickel, M. M., Zebhauser, P. T., Groß, J., & Ploner, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02525-0 · Cited by 84
Increased cortical reactivity to repeated tones at 8 months in infants with later ASD
Kolesnik, A., Begum Ali, J., Gliga, T., Guiraud, J., Charman, T., Johnson, M. H., Jones, E. J. H., & The BASIS Team (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0393-x · Cited by 75
Interregional alpha-band synchrony supports temporal cross-modal integration
van Driel, J., Knapen, T., van Es, D. M., & Cohen, M. X. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.07.022 · Cited by 66
Self-citation
Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery
Philips, G. R., Daly, J. J., & Príncipe, J. C. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0277-3 · Cited by 73
Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context
Lyu, M., Chen, W.-H., Ding, X., Wang, J., Pei, Z., & Zhang, B. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00067 · Cited by 73
Behavioural and neural markers of tactile sensory processing in infants at elevated likelihood of autism spectrum disorder and/or attention deficit hyperactivity disorder
Piccardi, E. S., Ali, J. B., Jones, E. J. H., Mason, L., Charman, T., Johnson, M. H., Gliga, T., BASIS/STAARS Team, Agyapong, M., Bazelmans, T., et al. (2021) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-020-09334-1 · Cited by 79
Conceptual, Regulatory and Strategic Imperatives in the Early Days of EEG-Based Biomarker Validation for Neurodevelopmental Disabilities
Ewen, J. B., Sweeney, J. A., & Potter, W. Z. (2019) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2019.00045 · Cited by 72
EPOS: EEG Processing Open-Source Scripts
Rodrigues, J., Weiss, M. H., Hewig, J. S., & Allen, J. J. B. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.660449 · Cited by 71
An Examination of the Neural Unreliability Thesis of Autism
Butler, J. S., Molholm, S., Andrade, G. N., & Foxe, J. J. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw375 · Cited by 75
Occipital alpha power reveals fast attentional inhibition of incongruent distractors
Janssens, C., Loof, E. D., Boehler, C. N., Pourtois, G., & Verguts, T. (2017) · Psychophysiology · doi:10.1111/psyp.13011 · Cited by 68
Endogenous sources of interbrain synchrony in duetting pianists
Gugnowska, K., Novembre, G., Kohler, N., Villringer, A., Keller, P. E., & Sammler, D. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab469 · Cited by 69
Resting state glutamate predicts elevated pre-stimulus alpha during self-relatedness: A combined EEG-MRS study on “rest-self overlap”
Bai, Y., Nakao, T., Xu, J., Qin, P.-M., Chaves, P., Heinzel, A., Duncan, N., Lane, T., Yen, N., Tsai, S., et al. (2016) · Social Neuroscience · doi:10.1080/17470919.2015.1072582 · Cited by 66
An excitatory cortical feedback loop gates retinal wave transmission in rodent thalamus
Murata, Y., & Colonnese, M. T. (2016) · eLife · doi:10.7554/elife.18816 · Cited by 70
Local and interregional alpha EEG dynamics dissociate between memory for search and memory for recognition
van Driel, J., Günseli, E., Meeter, M., & Olivers, C. N. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.031 · Cited by 73
Lasting connectivity increase and anxiety reduction via transcranial alternating current stimulation
Clancy, K. J., Baisley, S. K., Albizu, A., Kartvelishvili, N., Ding, M., & Li, W. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy096 · Cited by 68
CA2 neuronal activity controls hippocampal low gamma and ripple oscillations
Alexander, G. M., Brown, L. Y., Farris, S., Lustberg, D. J., Pantazis, C. B., Gloss, B. R., Plummer, N. W., Riddick, N. V., Moy, S. S., Jensen, P., et al. (2018) · eLife · doi:10.7554/elife.38052 · Cited by 74
Electrocortical measures of information processing biases in social anxiety disorder: A review
Harrewijn, A., Schmidt, L. A., Westenberg, P. M., Tang, A., & van der Molen, M. J. W. (2017) · Biological Psychology · doi:10.1016/j.biopsycho.2017.09.013 · Cited by 70
Non-selective inhibition of inappropriate motor-tendencies during response-conflict by a fronto-subthalamic mechanism
Wessel, J. R., Waller, D. A., & Greenlee, J. D. W. (2019) · eLife · doi:10.7554/elife.42959 · Cited by 67
Cognitive‐motor interference in the wild: Assessing the effects of movement complexity on task switching using mobile EEG
Reiser, J. E., Wascher, E., Rinkenauer, G., & Arnau, S. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14959 · Cited by 66
From pre-processing to advanced dynamic modeling of pupil data
Fink, L., Simola, J., Tavano, A., Lange, E. B., Wallot, S., & Laeng, B. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02098-1 · Cited by 80
Characterizing Focused Attention and Working Memory Using EEG
Mohamed, Z., Halaby, M. E., Said, T., Shawky, D., & Badawi, A. (2018) · Sensors · doi:10.3390/s18113743 · Cited by 69
Binaural Beats through the Auditory Pathway: From Brainstem to Connectivity Patterns
Perez, H. D. O., Dumas, G., & Lehmann, A. (2020) · eNeuro · doi:10.1523/eneuro.0232-19.2020 · Cited by 67
Behavioral and Electrophysiological Correlates of Memory Binding Deficits in Patients at Different Risk Levels for Alzheimer’s Disease
Pietto, M. L., Parra, M. A., Trujillo, N., Flores, F., García, A. M., Bustin, J., Richly, P., Manes, F. F., Lopera, F. J., Ibáñez, A. M., et al. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160056 · Cited by 66
Interictal network synchrony and local heterogeneity predict epilepsy surgery outcome among pediatric patients
Tomlinson, S. B., Porter, B. E., & Marsh, E. D. (2017) · Epilepsia · doi:10.1111/epi.13657 · Cited by 66
Adolescent cognitive control, theta oscillations, and social observation
Buzzell, G. A., Barker, T. V., Troller-Renfree, S. V., Bernat, E. M., Bowers, M. E., Morales, S., Bowman, L. C., Henderson, H. A., Pine, D. S., & Fox, N. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.077 · Cited by 65
Self-Generated Whisker Movements Drive State-Dependent Sensory Input to Developing Barrel Cortex
Dooley, J. C., Glanz, R. M., Sokoloff, G., & BLUMBERG, M. S. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.045 · Cited by 67
Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design
Wascher, E., Reiser, J. E., Rinkenauer, G., Larra, M. F., Dreger, F. A., Schneider, D. A., Karthaus, M., Getzmann, S., Gutberlet, M., & Arnau, S. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211007707 · Cited by 66
A comparative evaluation of signal quality between a research-grade and a wireless dry-electrode mobile EEG system
Marini, F., Lee, C. M., Wagner, J., Makeig, S., & Gola, M. K. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab21f2 · Cited by 65
Oscillatory Mechanisms of Preparing for Visual Distraction
de Vries, I. E. J., Savran, E., van Driel, J., & Olivers, C. N. L. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01460 · Cited by 61
Multiple Midfrontal Thetas Revealed by Source Separation of Simultaneous MEG and EEG
Zuure, M. B., Hinkley, L. B. N., Tiesinga, P. H. E., Nagarajan, S. S., & Cohen, M. X. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0321-20.2020 · Cited by 63
Self-citation
General Anesthesia Disrupts Complex Cortical Dynamics in Response to Intracranial Electrical Stimulation in Rats
Arena, A., Comolatti, R., Thon, S., Casali, A. G., & Storm, J. F. (2021) · eneuro · doi:10.1523/eneuro.0343-20.2021 · Cited by 66
Preliminary Results: The Impact of Smartphone Use and Short-Wavelength Light during the Evening on Circadian Rhythm, Sleep and Alertness
Höhn, C., Schmid, S. R., Plamberger, C. P., Bothe, K., Angerer, M., Gruber, G., Pletzer, B., & Hoedlmoser, K. (2021) · Clocks & Sleep · doi:10.3390/clockssleep3010005 · Cited by 61
Human temporal‐parietal junction spontaneously tracks others' beliefs: A functional near‐infrared spectroscopy study
Hyde, D. C., Betancourt, M. A., & Simon, C. E. (2015) · Human Brain Mapping · doi:10.1002/hbm.22953 · Cited by 66
Visual evoked feedforward–feedback traveling waves organize neural activity across the cortical hierarchy in mice
Aggarwal, A., Brennan, C. M., Luo, J., Chung, H., Contreras, D., Kelz, M. B., & Proekt, A. (2022) · Nature Communications · doi:10.1038/s41467-022-32378-x · Cited by 56
Selective Attention to Auditory Memory Neurally Enhances Perceptual Precision
Lim, S.-J., Wöstmann, M., & Obleser, J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2674-15.2015 · Cited by 67
Sure I'm Sure: Prefrontal Oscillations Support Metacognitive Monitoring of Decision Making
Wokke, M. E., Axel Cleeremans, B., & Ridderinkhof, K. R. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1612-16.2016 · Cited by 75
Top-down beta oscillatory signaling conveys behavioral context in early visual cortex
Richter, C. G., Coppola, R., & Bressler, S. L. (2018) · Scientific Reports · doi:10.1038/s41598-018-25267-1 · Cited by 73
Atypical Hemispheric Specialization for Faces in Infants at Risk for Autism Spectrum Disorder
Keehn, B., Vogel‐Farley, V., Tager‐Flusberg, H., & Nelson, C. A. (2015) · Autism Research · doi:10.1002/aur.1438 · Cited by 63
Spontaneous Neural Dynamics and Multi-scale Network Organization
Foster, B. L., He, B. J., Honey, C. J., Jerbi, K., Maier, A., & Saalmann, Y. B. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00007 · Cited by 63
Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans
Nickel, M. M., Dinh, S. T., May, E. S., Tiemann, L., Hohn, V. D., Groß, J., & Ploner, M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24784 · Cited by 68
Separating EEG correlates of stress: Cognitive effort, time pressure, and social‐evaluative threat
Ehrhardt, N. M., Fietz, J., Kopf‐Beck, J., Kappelmann, N., & Brem, A. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15211 · Cited by 65
Diagnosis of Alzheimer’s Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal
Amini, M., Pedram, M. M., Moradi, A., & Ouchani, M. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5511922 · Cited by 64
Intracranial source activity (eLORETA) related to scalp‐level asymmetry scores and depression status
Smith, E. E., Cavanagh, J. F., & Allen, J. J. B. (2017) · Psychophysiology · doi:10.1111/psyp.13019 · Cited by 68
Sustaining attention for a prolonged period of time increases temporal variability in cortical responses
Reteig, L. C., van den Brink, R. L., Prinssen, S., Cohen, M. X., & Slagter, H. A. (2019) · Cortex · doi:10.1016/j.cortex.2019.02.016 · Cited by 61
Self-citation
Converging Subjective and Psychophysiological Measures of Cognitive Load to Study the Effects of Instructor‐Present Video
Wang, J., Antonenko, P., Keil, A., & Dawson, K. (2020) · Mind Brain and Education · doi:10.1111/mbe.12239 · Cited by 64
Biometric Data as Real-Time Measure of Physiological Reactions to Environmental Stimuli in the Built Environment
Persiani, S. G. L., Kobaş, B., Koth, S. C., & Auer, T. (2021) · Energies · doi:10.3390/en14010232 · Cited by 67
Targeting neural oscillations with transcranial alternating current stimulation
Riddle, J., & Frӧhlich, F. (2021) · Brain Research · doi:10.1016/j.brainres.2021.147491 · Cited by 61
Superficial slow rhythms integrate cortical processing in humans
Halgren, M., Fabó, D., Ulbert, I., Madsen, J. R., Erőss, L., Doyle, W. K., Devinsky, O., Schomer, D., Cash, S. S., & Halgren, E. (2018) · Scientific Reports · doi:10.1038/s41598-018-20662-0 · Cited by 59
Visual attention, EEG alpha power and T7-Fz connectivity are implicated in prosthetic hand control and can be optimized through gaze training
Parr, J. V. V., Vine, S. J., Wilson, M., Harrison, N., & Wood, G. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0524-x · Cited by 63
Analyzing human sleep EEG: A methodological primer with code implementation
Cox, R., & Fell, J. (2020) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2020.101353 · Cited by 62
Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces
Leeuwis, N., Yoon, S., & Alimardani, M. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.732946 · Cited by 69
Trial‐by‐trial co‐variation of pre‐stimulus EEG alpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects
Benwell, C., Keitel, C., Harvey, M., Groß, J., & Thut, G. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13688 · Cited by 61
On the relevance of EEG resting theta activity for the neurophysiological dynamics underlying motor inhibitory control
Pscherer, C., Mückschel, M., Summerer, L., Bluschke, A., & Beste, C. (2019) · Human Brain Mapping · doi:10.1002/hbm.24699 · Cited by 57
Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis
Alotaibi, N. M., & Maharatna, K. (2021) · Neural Computation · doi:10.1162/neco_a_01394 · Cited by 60
Phase Alignment of Low-Frequency Neural Activity to the Amplitude Envelope of Speech Reflects Evoked Responses to Acoustic Edges, Not Oscillatory Entrainment
Oganian, Y., Kojima, K., Breska, A., Cai, C., Findlay, A. M., Chang, E. F., & Nagarajan, S. S. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1663-22.2023 · Cited by 59
Deep Convolutional Network Method for Automatic Sleep Stage Classification Based on Neurophysiological Signals
Sun, Y., Wang, B., Jin, J., & Wang, X. (2018) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2018.8633058 · Cited by 68
Optimal flickering light stimulation for entraining gamma waves in the human brain
Lee, K.-H., Park, Y., Suh, S. W., Kim, S., Kim, D.-W., Lee, J., Park, J., Yoo, S., & Kim, K. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-95550-1 · Cited by 58
Human subthalamic nucleus activity during non-motor decision making
Zavala, B., Jang, A. I., & Zaghloul, K. A. (2017) · eLife · doi:10.7554/elife.31007 · Cited by 57
Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson’s disease
Khawaldeh, S., Tinkhauser, G., Shah, S. A., Petermann, K., Debove, I., Nguyen, T. A. K., Nowacki, A., Lachenmayer, M. L., Schüpbach, M. W. M., Pollo, C., et al. (2019) · Brain · doi:10.1093/brain/awz417 · Cited by 54
How Attention Changes in Response to Incentives
Sawaki, R., Luck, S. J., & Raymond, J. E. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00847 · Cited by 57
Entrainment and maintenance of an internal metronome in supplementary motor area
Cadena-Valencia, J., García‐Garibay, O., Merchant, H., Jazayeri, M., & de Lafuente, V. (2018) · eLife · doi:10.7554/elife.38983 · Cited by 52
Theta rhythmicity governs human behavior and hippocampal signals during memory-dependent tasks
Wal, M. T., Domingo, J. L., Lifanov, J., Roux, F., Kolibius, L. D., Gollwitzer, S., Lang, J., Hamer, H. M., Rollings, D. T., Sawlani, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27323-3 · Cited by 62
Deep Learning Model With Adaptive Regularization for EEG-Based Emotion Recognition Using Temporal and Frequency Features
Samavat, A., Khalili, E., Ayati, B., & Ayati, M. (2022) · IEEE Access · doi:10.1109/access.2022.3155647 · Cited by 64
EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations
Hubbard, J., Kikumoto, A., & Mayr, U. (2019) · Scientific Reports · doi:10.1038/s41598-019-45333-6 · Cited by 55
Action information contributes to metacognitive decision-making
Wokke, M. E., Achoui, D., & Cleeremans, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-60382-y · Cited by 56
Fronto-temporal theta phase-synchronization underlies music-evoked pleasantness
Ara, A., & Marco‐Pallarés, J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116665 · Cited by 59
Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG
Hoy, C. W., Steiner, S. C., & Knight, R. T. (2021) · Communications Biology · doi:10.1038/s42003-021-02426-1 · Cited by 58
Dissociation of frontal‐midline delta‐theta and posterior alpha oscillations: A mobile EEG study
Liang, M., Starrett, M. J., & Ekstrom, A. D. (2018) · Psychophysiology · doi:10.1111/psyp.13090 · Cited by 53
Electroencephalogram-Based Motor Imagery Brain–Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering
Das, K., & Pachori, R. B. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3214081 · Cited by 56
Electroencephalogram
Casson, A. J., Abdulaal, M. J., Dulabh, M., Kohli, S., Krachunov, S., & Trimble, E. (2017) · Springer eBooks · doi:10.1007/978-3-319-69362-0_2 · Cited by 63
Time Series Decomposition into Oscillation Components and Phase Estimation
Matsuda, T., & Komaki, F. (2016) · Neural Computation · doi:10.1162/neco_a_00916 · Cited by 52
Transcranial photobiomodulation and thermal stimulation induce distinct topographies of EEG alpha and beta power changes in healthy humans
Wang, X., Wanniarachchi, H. I., Wu, A., Gonzalez-Lima, F., & Liu, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-97987-w · Cited by 54
Functional brain organization for number processing in pre‐verbal infants
Edwards, L. A., Wagner, J., Simon, C. E., & Hyde, D. C. (2015) · Developmental Science · doi:10.1111/desc.12333 · Cited by 55
Visuomotor Prediction Errors Modulate EEG Activity Over Parietal Cortex
Savoie, F.-A., Thénault, F., Whittingstall, K., & Bernier, P. (2018) · Scientific Reports · doi:10.1038/s41598-018-30609-0 · Cited by 51
Alpha-band oscillations track the retrieval of precise spatial representations from long-term memory
Sutterer, D., Foster, J. J., Serences, J. T., Vogel, E. K., & Awh, E. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00268.2019 · Cited by 54
A causal role for frontal cortico-cortical coordination in social action monitoring
Ninomiya, T., Noritake, A., Kobayashi, K., & Isoda, M. (2020) · Nature Communications · doi:10.1038/s41467-020-19026-y · Cited by 57
Signal Artifacts and Techniques for Artifacts and Noise Removal
Islam, M. K., Rastegarnia, A., & Sanei, S. (2021) · Intelligent Systems Reference Library · doi:10.1007/978-3-030-54932-9_2 · Cited by 54
Covert Spatial Attention Speeds Target Individuation
Foster, J. J., Bsales, E. M., & Awh, E. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2962-19.2020 · Cited by 50
Medial septal GABAergic neurons reduce seizure duration upon optogenetic closed-loop stimulation
Hristova, K., Martinez‐Gonzalez, C., Watson, T. C., Codadu, N. K., Hashemi, K. S., Kind, P. C., Nolan, M. F., & Gonzalez‐Sulser, A. (2021) · Brain · doi:10.1093/brain/awab042 · Cited by 53
A Neurophysiological Evaluation of Cognitive Load during Augmented Reality Interactions in Various Industrial Maintenance and Assembly Tasks
Alessa, F. M., Alhaag, M. H., Al-Harkan, I. M., Ramadan, M. Z., & Alqahtani, F. M. (2023) · Sensors · doi:10.3390/s23187698 · Cited by 63
Human NMDAR autoantibodies disrupt excitatory-inhibitory balance, leading to hippocampal network hypersynchrony
Ceangă, M., Rahmati, V., Haselmann, H., Schmidl, L., Hunter, D., Brauer, A.-K., Liebscher, S., Kreye, J., Prüß, H., Groc, L., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113166 · Cited by 55
Immersion in nature enhances neural indices of executive attention
McDonnell, A. S., & Strayer, D. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-52205-1 · Cited by 67
Behavioral and EEG responses to social evaluation: A two-generation family study on social anxiety
Harrewijn, A., van der Molen, M. J. W., van Vliet, I. M., Tissier, R., & Westenberg, P. M. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.11.010 · Cited by 53
Resting State Electroencephalography and Sports-Related Concussion: A Systematic Review
Conley, A. C., Cooper, P. S., Karayanidis, F., Gardner, A. J., Levi, C. R., Stanwell, P., Gaetz, M., & Iverson, G. L. (2018) · Journal of Neurotrauma · doi:10.1089/neu.2018.5761 · Cited by 52

Authors: Cohen, M. X.
Venue: The MIT Press eBooks
Publisher: The MIT Press
Published: 2014-01-17
DOI: 10.7551/mitpress/9609.001.0001
OpenAlex: W4230797216
Cited by: 2,105
References: 0
Citations by year: 2026: 226 · 2025: 277 · 2024: 237 · 2023: 258 · 2022: 226 · 2021: 221 · 2020: 226 · 2019: 149 · 2018: 108 · 2017: 85 · 2016: 51 · 2015: 31 · 2014: 6
FWCI: 20.41
Type: book
Language: en
Open access: closed
Locations: The MIT Press eBooks (publishedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Social Sciences › Education › Undergraduate Neuroscience Education and Research
Keywords: magnetoencephalography, EEG, local field potentials, time-frequency analysis, synchronization analysis, Fourier transform, convolution, Euler's formula, group-level analysis, single-subject analysis, brain electrical signals

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Analyzing Neural Time Series Data: Theory and Practice

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks