The DOI matches its Crossref record; 263 of the 267 references it cites were found in a registry or an index, and 1 are not checked yet.
EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies
Open accessAbstract
A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“A recent review of the literature by Newson and Thiagarajan (2018) revealed that atypical EEG profiles, such as increased power at lower frequencies and decreased power at higher frequencies, seem to be shared by individuals with different conditions (e.g., ADHD, OCD and schizophrenia) and not specific to ADHD.”
From Is autonomic nervous system function atypical in Attention Deficit Hyperactivity Disorder (ADHD)? A systematic review of the evidence (2020) · cites this paper as Newson and Thiagarajan (2018)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hamilton, M. (1960) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.23.1.56 · Cited by 32,817 · cited 1×
“Beyond more conventional DSM/ICD measures, depression diagnosis and severity was most typically measured using the Hamilton Rating Scale for Depression (HAM-D; Hamilton, 1960).”
MethodAdvances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
The Positive and Negative Syndrome Scale (PANSS) for schizophrenia.
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987) · Schizophrenia Bulletin · doi:10.1093/schbul/13.2.261 · Cited by 21,378
The Brief Psychiatric Rating Scale.
Overall, J. E., & Gorham, D. R. (1962) · Psychological Reports · doi:10.2466/pr0.1962.10.3.799 · Cited by 11,090
Lord, C., Rutter, M., & Le Couteur, A. (1994) · Journal of Autism and Developmental Disorders · doi:10.1007/bf02172145 · Cited by 9,065
The Yale-Brown Obsessive Compulsive Scale: I. Development, Use, and Reliability
Goodman, W. K. (1989) · Archives of General Psychiatry · doi:10.1001/archpsyc.1989.01810110048007 · Cited by 7,712
Internet Addiction: The Emergence of a New Clinical Disorder
YOUNG, K. S. (1998) · CyberPsychology & Behavior · doi:10.1089/cpb.1998.1.237 · Cited by 5,513
The worldwide prevalence of ADHD: a systematic review and metaregression analysis
Polanczyk, G., de Lima, M. S., Horta, B. L., Biederman, J., & Rohde, L. A. (2007) · American Journal of Psychiatry · doi:10.1176/ajp.2007.164.6.942 · Cited by 5,346
The development of a Clinician-Administered PTSD Scale
Blake, D. D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Gusman, F. D., Charney, D. S., & Keane, T. M. (1995) · Journal of Traumatic Stress · doi:10.1002/jts.2490080106 · Cited by 5,024
Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266
Flagged first, then by citations
3 of the 267 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Achenbach, 2001, Manual for the ASEBA School-Age Forms and Profiles: An Integrated System of Mult-Informant Assessment.
- Conners, 1999, Conners’ Adult ADHD Rating Scales (CAARS).
- Rutter, 2003, The Social Communication Questionnaire: Manual.
Cited by
Sorted by citations
Items 601–700 of 865
Multi-source Information Fusion for Depression Detection
Wang, R., Wang, H., Hu, Y., Lin, W., & Ma, H. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8469-5_41 · Cited by 1
Moussiopoulou, J., Handrack‐Bonnet, M., Pross, B., Pogarell, O. M., Keeser, D., Halle, M., Falkai, P., Scherr, J., Hasan, A., & Roeh, A. (2024) · Brain and Behavior · doi:10.1002/brb3.3480 · Cited by 1
Алексеевич, Г. С., Kisel, N. I., & Bokhan, N. A. (2024) · Psikhiatriya · doi:10.30629/2618-6667-2024-22-2-39-45 · Cited by 1
Jaworska, N., de la Salle, S., Schryver, B., Birmingham, M., Phillips, J. L., Blier, P., & Knott, V. (2024) · Clinical EEG and Neuroscience · doi:10.1177/15500594241294021 · Cited by 1
Transcranial magnetic stimulation (rTMS) in the treatment of depressive disorders
Лебедева, И. С., Николаевич, П. А., Panikratova, Y. R., Гаяровна, А. Е., & Каледа, В. Г. (2024) · S S Korsakov Journal of Neurology and Psychiatry · doi:10.17116/jnevro202412410155 · Cited by 1
Sahu, R., Pattnaik, P. K., Anbananthen, K. S. M., & Muthaiyah, S. (2025) · IEEE Access · doi:10.1109/access.2025.3552619 · Cited by 1
Methods for Brain Connectivity Analysis with Applications to Rat Local Field Potential Recordings
El-Yaagoubi, A. B., Aslan, S., Gomawi, F., Redondo, P. V., Roy, S., Sultan, M. S., Talento, M. S., Tarrazona, F. T., Wu, H., Cooper, K. W., et al. (2025) · Entropy · doi:10.3390/e27040328 · Cited by 1
Transcranial Magnetic Stimulation in the Treatment of Depressive Disorders
Лебедева, И. С., Николаевич, П. А., Panikratova, Y. R., Гаяровна, А. Е., & Каледа, В. Г. (2025) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-025-01801-3 · Cited by 1
Evaluating the efficacy of three classical EEG paradigms in the discrimination of bipolar depression
Yang, C., Pi, Y., Wang, W., Huang, Y., Tang, N., Wang, H., & Wen, S. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1545132 · Cited by 1
Lum, J. A. G., Hamilton, K., Fuelscher, I. T., Barhoun, P., Deconinck, F. J. A., Raeve, A. D., Ford, T. C., Silk, T. J., Enticott, P. G., Kumar, G., et al. (2025) · Psychophysiology · doi:10.1111/psyp.70084 · Cited by 1
Establishing validity standards for iPSC modeling of neuropsychiatric disorders
Kolsters, N., Vernon, A. C., Kasri, N. N., & Latour, B. L. (2025) · Genomic psychiatry : · doi:10.61373/gp025p.0074 · Cited by 1
Bhadra, K., Janakiram, A. A., Marco, S., Micali, N., Hüppi, P. S., & Berchio, C. (2025) · Brain Topography · doi:10.1007/s10548-025-01149-4 · Cited by 1
Song, K., Liu, Y., & Xu, P. (2025) · Brain and Behavior · doi:10.1002/brb3.70977 · Cited by 1
Electroencephalogram Gamma Band Power Correlates with Anhedonia in a Community Sample
Coleman, S. L., Evans, I. D., Sharpley, C. F., Bitsika, V., Odierna, G. L., & Vessey, K. A. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15110536 · Cited by 1
del Cerro‐León, A., Uceta, M., Shpakivska‐Bilán, D., Suárez‐Méndez, I., Peribáñez‐Baz, H., Cuesta, P., Bruña, R., García‐Moreno, L. M., Maestú, F., & Antón‐Toro, L. F. (2025) · Addiction · doi:10.1111/add.70246 · Cited by 2
Mori, S., Hoshino, A., Uemura, J.-I., Sano, M., Nishiura, Y., Morikawa, I., Iwatsuki, K., Hirata, H., & Hoshiyama, M. (2025) · Clinical EEG and Neuroscience · doi:10.1177/15500594251387497 · Cited by 1
Kavanaugh, B. C., Vigne, M. M., Gamble, I., Legere, C. H., DePamphilis, G., Acuff, W. L., Tirrell, E., Vaughan, N., Thorpe, R. V., Festa, E. K., et al. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01361-6 · Cited by 2
Resting-State Theta/Beta Ratio Reveals Distinct Neural Signatures in High Sign-Tracking Individuals
Mattioni, L., Gregorio, F. D., Badioli, M., Danti, C., Degni, L., Finotti, G., Starita, F., di Pellegrino, G., & Garofalo, S. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.12.001 · Cited by 1
What Are Gamma Waves (And How Are They Relevant to Clinical Psychiatry)?
Rocks, D. L., Sharpley, C. F., Bitsika, V., & Odierna, G. L. (2025) · Applied Sciences · doi:10.3390/app152312668 · Cited by 1
Spectral power of the beta rhythm of the electroencephalogram as a marker of depressive disorder
Алексеевич, Г. С., Васильева, С. Н., Simutkin, G. G., Ivanova, S. А., & Bokhan, N. A. (2021) · Neurology Bulletin · doi:10.17816/nb54539 · Cited by 1
Watson, C. B., Sharpley, C. F., & Bitsika, V. (2025) · NeuroSci · doi:10.3390/neurosci7010003 · Cited by 1
EEG-Based Depression Detection Using CNNs and Heatmaps
Sadiq, M. R., Islam, T., Esha, M. N.-A. M., Hasan, M. M., Johab, M. A., & Ahmed, M. R. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-11335-1_18 · Cited by 1
Dirik, H. B. (2025) · Gazi Beden Eğitimi ve Spor Bilimleri Dergisi · doi:10.53434/gbesbd.1793781 · Cited by 1
Woronko, S. E., Qian, A., Carlton, C. N., Lees, T., & Kujawa, A. (2026) · Psychophysiology · doi:10.1111/psyp.70226 · Cited by 1
Cross-Subject EEG Mental State Recognition via Correlation-Based Feature Selection
Odake, E. M., Faria, D. R., & Ribeiro, E. P. (2026) · Applied Sciences · doi:10.3390/app16021011 · Cited by 1
Vos, G., Ebrahimpour, M., van Eijk, L., Sarnyai, Z., & Azghadi, M. R. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109708 · Cited by 1
Liu, Y., Siekierski, P., Stefano, L. A. D., Westerkamp, G. C., Ethridge, L. E., Blank, E., Miyakoshi, M., Thorpe, R. V., Jones, S. R., Erickson, C. A., et al. (2026) · Clinical Neurophysiology · doi:10.1016/j.clinph.2026.2111713 · Cited by 1
Ghanizadeh, A., Bajestani, G. S., Makhloughi, F., Hosseini, A., Toosi, M. B., & Kharrazi, F. H. (2026) · Current Psychology · doi:10.1007/s12144-026-09191-6 · Cited by 2
Excessive gamma and beta oscillations in manic states across mood and psychotic disorders
Yanagi, M., Iwasaki, T., Iwamura, Y., Ichikawa, O., Ishida, S., Shirakawa, O., Hashimoto, M., & Ikeda, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-40673-6 · Cited by 1
Manea, C., Colareza, C. C., Rad, D., Bocoș, M.-D., Panc, T., Hammami, M., & Banariu, G. M. (2026) · Brain Sciences · doi:10.3390/brainsci16030311 · Cited by 2
Xue, G., Chen, B., Zhang, Y., & Hou, L. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105026 · Cited by 1
Iglesias‐Cabeza, N., Zafra‐Puerta, L., Worby, C. A., Burgos, D. F., Sciaccaluga, M., Imperatore, V., Fermont, L., Sánchez‐Martín, G., Mitra, S., Gentry, M. S., et al. (2026) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2026.102912 · Cited by 1
Wang, R., Ouyang, W., Chen, J., Jiang, G.-P., & Liu, X. (2026) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-026-00171-4 · Cited by 1
Multimodal EEG–MRI Neuroimaging in Schizophrenia—A Systematic and Mechanistic Review
Chmiel, J., & Kopańska, M. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15114306 · Cited by 1
Gancio, J., López, N. L., Pons, A. J., Tirabassi, G., & Masoller, C. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0326211 · Cited by 1
Seo, Y. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27136084 · Cited by 1
Sahab, A., Asfar, M., Machin, A., Wicaksana, I. G. W., & Romadhona, M. K. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02967-9 · Cited by 1
Ghani, U., Amjad, I., Niazi, I. K., Kumari, N., Holt, K. R., Ashfaque, M., Pujari, A. N., Kamavuako, E. N., Murphy, B. A., & Haavik, H. (2026) · Brain Sciences · doi:10.3390/brainsci16080818 · Cited by 1
Zani, A. (2020) · Brain Sciences · doi:10.3390/brainsci10060350 · Cited by 1
Markovics, J. A. (2021) · Artificial intelligence · doi:10.5772/intechopen.98343 · Cited by 1
Kirabira, J., Rukundo, G. Z., & Kibuuka, M. (2022) · The International Journal of Psychiatry in Medicine · doi:10.1177/00912174211058136 · Cited by 1
Hunger: A Strong Basic Drive that Controls Human Emotions
Bhanumathi, K. S., & Jayadevappa, D. (2021) · International Conference on Computational Performance Evaluation (ComPE) · doi:10.1109/compe53109.2021.9752175 · Cited by 1
Kipiński, L., Maciejowski, A., Małyszczak, K., & Pilecki, W. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109626 · Cited by 1
A Dynamic Systems View of Brain Waves
Griffith, T. D., Hubbard, J. E., & Balas, M. J. (2023) · Synthesis lectures on biomedical engineering · doi:10.1007/978-3-031-23529-0_2 · Cited by 1
Yamamoto, Y., Harachi, K., Muramatsu, A., Nagahara, H., Takemura, N., Mizuno‐Matsumoto, Y., & Shimojo, S. (2023) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.143.430 · Cited by 1
Joksimovic, S. M., & Joksimovic, S. L. (2023) · Arhiv za farmaciju · doi:10.5937/arhfarm73-43104 · Cited by 1
The EEG Study on Addictive Inducement of Game Disorder in Undergraduate Students
Yu, Z., Li, L., Zhang, Y., Lin, M., Wu, Z., Wang, Z., Kuang, S., & Xu, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3277101 · Cited by 1
[Electroencephalographic microstates in vestibular schwannoma patients with tinnitus].
Zhang, C., Wang, X., Ding, Z., Zhou, H., Liu, P., Xue, X., Cao, W., Zhu, Y., Chen, J., Shen, W., et al. (2023) · PubMed Central · doi:10.12122/j.issn.1673-4254.2023.05.15 · Cited by 1
Kose, M. R., Ahirwal, M. K., & Atulkar, M. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0085-5_2 · Cited by 1
Cavicchioli, M., Galbiati, A., Tobia, V., & Ogliari, A. L. (2023) · Journal of Addictive Diseases · doi:10.1080/10550887.2023.2232252 · Cited by 1
Major depressive disorder: Brain signals as biomarkers for depression
Costa, T. L. (2023) · Open Access Government · doi:10.56367/oag-040-10653 · Cited by 1
Internet of Things-Combined Deep Learning for Electroencephalography-Based E-Healthcare
Das, S., Ghosh, A., & Saha, S. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-3679-3.ch011 · Cited by 1
Bibliometric analysis of neurofeedback research from 2000 to 2022
Ali, S. A., Ghazali, M. M., Aziz, N. A. A., & Nisar, H. (2024) · Neuroscience Research Notes · doi:10.31117/neuroscirn.v7i1.265 · Cited by 1
The Physiologic Basis of Neuromodulation
Wilson, T. J., & Butala, A. (2024) · Advances in Psychiatry and Behavioral Health · doi:10.1016/j.ypsc.2024.05.001 · Cited by 1
3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness
Zhuang, W., Zhang, Y., Wang, Y., & He, K. (2024) · Sensors · doi:10.3390/s24237856 · Cited by 1
Iftimovici, A., Jardri, R., Lefebvre, A., Mallet, J., & Antoine Lefrere, A. (2025) · médecine/sciences · doi:10.1051/medsci/2025071 · Cited by 1
Neural Electrical Correlates of Subjective Happiness
Sato, W., Kochiyama, T., & Uono, S. (2025) · Human Brain Mapping · doi:10.1002/hbm.70224 · Cited by 2
Francis, A. M., Anderson, T.-J., McKearney, K. J., Schryver, B., Tibbo, P. G., & Fisher, D. J. (2025) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2025.112006 · Cited by 1
Forbes, O., Santos–Fernández, E., Wu, P. P., & Mengersen, K. L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0326598 · Cited by 1
Chang, W.-S., Liang, W., Huang, N. E., Nguyen, K. T., & Juan, C.-H. (2025) · Biological Psychology · doi:10.1016/j.biopsycho.2025.109099 · Cited by 1
EEG Signatures of COVID-19 Survival compared to close contacts and the Cuban EEG normative database
Calzada‐Reyes, A., Galán‐Garcia, L., Virues-Alba, T. A., Ruíz, L. C., Perez-Mayo, L., Bringas-Vega, M. L., Ren, P., Bosch‐Bayard, J., Acosta-Imas, Y., Vega‐Hernández, M., et al. (2026) · Clinical Neurophysiology Practice · doi:10.1016/j.cnp.2026.02.007 · Cited by 3
Prediction of cortical theta oscillations in humans for phase-locked visual stimulation
Brügger, D., & Abegg, M. H. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109288 · Cited by 1
Téllez-Villagra, C., & Avilés, A. G. P. (2021) · Revista de Psiquiatría Infanto-Juvenil · doi:10.31766/revpsij.v38n2a2 · Cited by 1
Yamamoto, Y., TANAKA, S., Harachi, K., Muramatsu, A., Takemura, N., Nagahara, H., Mizuno‐Matsumoto, Y., & Shimojo, S. (2022) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.34.3_654 · Cited by 1
Nguyen, L. T. H., Nguyen, N. P. K., Tran, K. N., Shin, H., & Yang, I. (2023) · 동의생리병리학회지 · doi:10.15188/kjopp.2023.04.37.2.36 · Cited by 1
Adaptive Frequency Band Analysis for Functional Time SeriesPreprint
Bagchi, P., & Bruce, S. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01784 · Cited by 1
Assadourian, S., Lopes, A. B., & Saj̈, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.13.472447 · Cited by 1
Trejo, L. J., Rosipal, R., Moore, A., Lujan, B., Kosheleff, A. R., Ereshefsky, L., Owen, R., & Vlasuk, G. P. (2021) · doi:10.31234/osf.io/hu7ba · Cited by 1
Kim, K. M., Bong, S. H., & Kim, J. W. (2021) · Research Square · doi:10.21203/rs.3.rs-775299/v1 · Cited by 1
Sela, Y., Santamaria, L., Amichai‐Hamburger, Y., & Leong, V. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/x9wpc · Cited by 1
Jamal, R., & Kullar, M. (2022) · doi:10.31234/osf.io/c957d · Cited by 1
Effects of Different Types of Neurofeedback on Emotional Arousal: A Pilot Study
Markovitz, A. (2022) · Open Access Victoria University of Wellington | Te Herenga Waka (Figshare) · doi:10.26686/wgtn.21373194 · Cited by 1
It’s time to RELAX and smell the roses!
Blasio, F. M. D., & Barry, R. J. (2023) · Clinical Neurophysiology · doi:10.1016/j.clinph.2023.02.169 · Cited by 1
Arikumar, K. S., Tamilarasi, K., Baza, M., Srivast, G., Alsabaan, M., Gadekallu, T. R., Prathiba, S. B., & Senthilkumar, G. (2023) · Research Square · doi:10.21203/rs.3.rs-2753475/v1 · Cited by 1
Miyatsu, T., Mahyari, A., Rogala, J., Broderick, T. J., & Pirolli, P. L. T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.07.622537 · Cited by 1
Xiao, W., Jones, M., Milne, E., & Dede, A. J. O. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.13.632659 · Cited by 1
Electrophysiological sex-dimorphism as early risk markers of alcohol use in adolescencePreprint
del Cerro‐León, A., Uceta, M., Shpakivska‐Bilán, D., Suárez‐Méndez, I., Peribáñez‐Baz, H., Cuesta, P., Bruña, R., García‐Moreno, L. M., Maestú, F., & Antón‐Toro, L. F. (2025) · Research Square · doi:10.21203/rs.3.rs-6057213/v1 · Cited by 1
Sample size critically shapes the reliability of EEG case-control findings in psychiatryPreprint
Ebadi, A., Kaderi, S., Rodríguez‐Herreros, B., Chabane, N., Allouch, S., & Hassan, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.10.687610 · Cited by 1
Evaluating Resting State EEG Biomarkers Across Psychosis Biotypes: Stability and HD-tDCS ModulationPreprint
Trotti, R. L., Doss, I., Parker, D. A., Raymond, N., Sauer, K., Pearlson, G. D., Gershon, E. S., Keedy, S., Hill, S. K., Tamminga, C. A., et al. (2026) · medRxiv · doi:10.64898/2026.02.23.26346924 · Cited by 1
Modeling high-dimensional time series from large scale brain networks
do Nascimento, D. C. (2020) · doi:10.11606/t.104.2020.tde-23072020-155937
Statistical Learning for the Spectral Analysis of Time Series Data
Tuft, M. (2020) · D-Scholarship@Pitt (University of Pittsburgh)
Fredes, P., & Raff, U. (2020) · Springer eBooks · doi:10.1007/978-3-030-47645-8_10
Лебедева, И. С., Golubev, S. A., Klochkova, I. V., & Каледа, В. Г. (2020) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-020-01016-8
Giuliani, L., Koutsouleris, N., Vignapiano, A., Koenig, T. W., Mucci, A., Giordano, G. M., Reuf, A., Altamura, M., Bellomo, A., Brugnoli, R., et al. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.09.575
Chen, I., Chen, C., Chang, C.-H., Fan, Z.-C., Chang, Y., Lin, D., Lin, C., & Ko, L. (2020) · Research Square · doi:10.21203/rs.3.rs-125027/v1
Cognitive Abilities in Hearing Loss
Gottschalk, K. E. (2021) · UKnowledge (University of Kentucky) · doi:10.13023/etd.2021.008
Maitra, S., Chatterjee, M., Sasidharan, A., Sinha, S., & Mukhopadhyay, K. (2020) · Neural Network World · doi:10.14311/nnw.2020.30.024
Electrophysiology: From Molecule to Cognition, from Animal to Human
Sambeth, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-62351-7_9
Continuous miner dust collector proves worth at Eastover
Hundman, G. J., Disney, P., & Divers, E. (1983) · Coal Min. Process.; (United States)
Proceedings of the 2021 ISNR Annual Conference (Virtual): Poster Presentations
International Society of NeuroRegulation and Research (2021) · NeuroRegulation · doi:10.15540/nr.8.4.220
Does brain maping lead the way in mood disorders? (tur)
Yumru, M. (2021) · Journal of Clinical Psychiatry · doi:10.5505/kpd.2021.38991
Conectividad de la red cerebral en el trastorno bipolar y la esquizofrenia
Cañas, B. B. C. (2021) · UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) · doi:10.35376/10324/51849 · Cited by 1
GSK-3β disrupts neural oscillatory function to inhibit learning and memory in ratsPreprint
Albeely, A. M., Williams, O. O. F., & Perreault, M. L. (2019) · doi:10.21203/rs.2.18860/v1
Button Press Detection from EEG Signals Using Deep Learning
Kuzucu, E. E. (2022) · International Siberian workshops and tutorials on electron devices and materials · doi:10.1109/edm55285.2022.9855191
Impact of Sampling Rate and Eye-Condition on Resting Quantitative EEG
Neto, F. S. D. A., & Rosa, J. L. G. (2022) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · doi:10.23919/spa53010.2022.9927794
Maxion, A., Gaebler, A. J., Röhrig, R., Mathiak, K., Zweerings, J., & Kutafina, E. (2023) · doi:10.2196/preprints.46376
Aggarwal, S., & Ray, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.15.528644
Metadata
- Authors
- Newson, J. J., & Thiagarajan, T. C.
- Affiliations
- Sapien Labs (US)
- Corresponding author
- Thiagarajan, Tara C.
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2019-01-09
- Volume
- 12
- Pages
- 521-521
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC6333694
- OpenAlex
- W2908954394
- Cited by
- 863
- References
- 267
- Citations by year
- 2026: 127 · 2025: 134 · 2024: 158 · 2023: 147 · 2022: 111 · 2021: 96 · 2020: 69 · 2019: 19
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- EEG frequency bands, resting-state EEG, mental illness, EEG spectral power, depression, attention deficit hyperactivity disorder, autism spectrum disorder, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder, schizophrenia, symptom severity
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