The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment
Abstract
In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Algorithms have been developed for estimating functional connectivity from electrophysiological source signals derived from EEG (or MEG), including directed transfer function (DTF) algorithm (98), the adaptive DTF (ADTF) (99–101), direct DTF (dDTF) (102), and partial directed coherence (PDC) (103).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [99]
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Random Data-Analysis and Measurement Procedures
Bendat, J. S., Piersol, A. G., & Saunders, H. (1989) · Journal of vibration and acoustics · doi:10.1115/1.3269865 · Cited by 6,401
Spectral Analysis and its Applications.
Freiberger, W. F., Jenkins, G. M., & Watts, D. G. (1969) · Journal of the American Statistical Association · doi:10.2307/2286100 · Cited by 4,990
Spectral Analysis for Physical Applications
Percival, D. B., & Walden, A. T. (1993) · Cambridge University Press eBooks · doi:10.1017/cbo9780511622762 · Cited by 2,148
Episodic multiregional cortical coherence at multiple frequencies during visual task performance
Bressler, S. L., Coppola, R., & Nakamura, R. (1993) · Nature · doi:10.1038/366153a0 · Cited by 533
Self-citationRecursive Multichannel Maximum Entropy Spectral Estimation
Morf, M., Vieira, A., Lee, D., & Kailath, T. (1978) · IEEE transactions on geoscience electronics · doi:10.1109/tge.1978.294569 · Cited by 259
Spectral analysis methods for neurological signals
Muthuswamy, J., & Thakor, N. V. (1998) · Journal of Neuroscience Methods · doi:10.1016/s0165-0270(98)00065-x · Cited by 196
Spectral analysis of EEG's by autoregressive decomposition of time series
Gersch, W. (1970) · Mathematical Biosciences · doi:10.1016/0025-5564(70)90049-0 · Cited by 150
Franaszczuk, P. J., Blinowska, K. J., & Kowalczyk, M. (1985) · Biological Cybernetics · doi:10.1007/bf00337149 · Cited by 137
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–500 of 582
Mahmood, A., Steindler, J. R., & Katz, D. B. (2026) · Journal of Neurophysiology · doi:10.1152/jn.00503.2025 · Cited by 10
A dynamic directed transfer function for brain functional network-based feature extraction
Li, M., & Zhang, N. (2022) · Brain Informatics · doi:10.1186/s40708-022-00154-8 · Cited by 7
Top-Down Cortical Influences in Visual Expectation
Bressler, S. L., Richter, C. G., Chen, Y., & Ding, M. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246678 · Cited by 7
Self-citationExploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)
Duan, K., Xie, S., Zhang, X., Xie, X., Cui, Y., Liu, R., & Xu, J. (2023) · Brain Sciences · doi:10.3390/brainsci13020247 · Cited by 7
Evaluation and comparison of effective connectivity during simple and compound limb motor imagery
Yi, W., Zhang, L., Wang, K., Xiao, X., He, F., Zhao, X., Qi, H., Zhou, P., Wan, B., & Ming, D. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944720 · Cited by 7
Estimation of Vector Autoregressive Parameters and Granger Causality From Noisy Multichannel Data
Rangarajan, P., & Rao, R. P. N. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2885812 · Cited by 6
Whale optimization algorithm fused with SVM to detect stress in EEG signals
Gupta, R., Alam, M., & Agarwal, P. (2021) · Intelligent Decision Technologies · doi:10.3233/idt-200047 · Cited by 7
Coherence and Phase Synchrony Analysis of Electroencephalogram
Tcheslavski, G. V. (2005) · Cited by 7
Jia, H. (2019) · Springer eBooks · doi:10.1007/978-981-13-9113-2_12 · Cited by 7
Spectrally and temporally resolved estimation of neural signal diversityPreprint
Mediano, P. A. M., Rosas, F. E., Luppi, A. I., Noreika, V., Seth, A. K., Carhart-Harris, R. L., Barnett, L., & Bor, D. (2023) · doi:10.7554/elife.88683 · Cited by 9
Time courses of brain plasticity underpinning visual motion perceptual learning
Song, Y., Wang, Q., & Fang, F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120897 · Cited by 6
Sparks, H. D., Cross, K. A., Choi, J. W., Courellis, H. S., Thum, J. A., Koenig, E., & Pouratian, N. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.11.077 · Cited by 6
Dual-functional Network Regulation Underlies the Central Executive System in Working Memory
Yu, R., Han, B., Wu, X., Wei, G., Zhang, J., Ding, M., & Wen, X. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.05.025 · Cited by 8
Self-citationApplications of Granger Causality Model to Connectivity Network Based on fMRI Time Series
Wen, X., Zhao, X., Yao, L., & Wu, X. (2006) · Lecture notes in computer science · doi:10.1007/11881070_31 · Cited by 6
Lateralization of Epileptic Foci Through Causal Analysis of Scalp-EEG Interictal Spike Activity
Li, W., Wu, N., Hou, W., Zhang, J., Xu, W., Wu, G.-C., Zheng, X., & Feng, H. (2015) · Journal of Clinical Neurophysiology · doi:10.1097/wnp.0000000000000120 · Cited by 6
Causal Inference Based on the Analysis of Events of Relations for Non-stationary Variables
Yin, Y., & Yao, D. (2016) · Scientific Reports · doi:10.1038/srep29192 · Cited by 6
Xue, F., Yue, X., Fan, Y., Cui, J., Brauth, S. E., Tang, Y., & Fang, G. (2018) · Journal of Experimental Biology · doi:10.1242/jeb.167775 · Cited by 6
Evolutionary Spectra Based on the Multitaper Method with Application To Stationarity Test
Xiang, Y., Ding, J., & Tarokh, V. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461443 · Cited by 6
Significance of event related causality (ERC) in eloquent neural networks
Korzeniewska, A., Mitsuhashi, T., Wang, Y., Asano, E., Franaszczuk, P. J., & Crone, N. E. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.02.002 · Cited by 6
Propagation of EEG activity during continuous attention test
Kuś, R., Blinowska, K. J., Kamiński, M., & Basińska-Szafrańska, A. (2005) · Bulletin of the Polish Academy of Sciences Technical Sciences · Cited by 6
Ginter, J., Blinowska, K. J., Kamiński, M., & Kuś, R. (2005) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2005.1419564 · Cited by 6
Weiss, A. R., Korzeniewska, A., Chrabaszcz, A. V., Bush, A., Fiez, J. A., Crone, N. E., & Richardson, R. M. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00086 · Cited by 5
Exploring Causality Within Collaborative Problem Solving Using Eye-Tracking
Sharma, K., Olsen, J. K., Aleven, V., & Rummel, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98572-5_32 · Cited by 5
Face-Specific Activity in the Ventral Stream Visual Cortex Linked to Conscious Face Perception
Li, W., Cao, D., Li, J., & Jiang, T. (2024) · Neuroscience Bulletin · doi:10.1007/s12264-024-01185-3 · Cited by 13
Chao, Z. C., & Fujii, N. (2013) · Springer eBooks · doi:10.1007/978-4-431-54331-2_3 · Cited by 5
Quantitative measures of cortical functional connectivity: A state-of-the-art brief survey
Kurgansky, А. V. (2013) · Human Physiology · doi:10.1134/s0362119713030134 · Cited by 5
Yan, J., Guo, X., Sun, J., & Tong, S. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090333 · Cited by 5
Toppi, J., Petti, M., Mattia, D., Babiloni, F., & Astolfi, L. (2014) · Neuromethods · doi:10.1007/7657_2014_69 · Cited by 5
Connectivity measures in the Poffenberger paradigm indicate hemispheric asymmetries
Erbil, N. (2016) · Functional Neurology · doi:10.11138/fneur/2016.31.4.249 · Cited by 5
Multiscale Bayesian state-space model for Granger causality analysis of brain signal
Cekic, S., Grandjean, D., & Renaud, O. (2018) · Journal of Applied Statistics · doi:10.1080/02664763.2018.1455814 · Cited by 6
Fernandes, T., Direito, B., Sayal, A., Pereira, J., Andrade, A., & Castelo-Branco, M. S. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108758 · Cited by 5
Causally linking neural dominance to perceptual dominance in a multisensory conflict
Yun, K., Bhattacharya, J., Sandkühler, S., Lin, Y.-J., Iwaki, S., & Shinsuke Shimojo, A. (2020) · Neuroreport · doi:10.1097/wnr.0000000000001505 · Cited by 5
Differential modulation of thalamo-parietal interactions by varying depths of isoflurane anesthesia
Cho, D., Shin, T. J., Ham, J., Choi, D., Kim, S., Jeong, S., Kim, H.-I., Kim, J. G., & Lee, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0175191 · Cited by 5
Niu, W., Jiang, Y., Zhang, X., Jiang, T., Zhang, Y., & Yu, S. (2020) · Neuroscience Bulletin · doi:10.1007/s12264-020-00550-2 · Cited by 5
Single trial classification of magnetoencephalographic recordings using Granger causality
Kostelecki, W., Dominguez, L. G., & Velázquez, J. L. P. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.04.032 · Cited by 5
Causal inference in neuronal time-series using adaptive decomposition
Rodrigues, J., & Andrade, A. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.02.013 · Cited by 5
Identification of directed interactions in networks
Lindsay, K. A., & Rosenberg, J. R. (2011) · Biological Cybernetics · doi:10.1007/s00422-011-0437-7 · Cited by 5
Semiparametric detection of nonlinear causal coupling using partial directed coherence
Massaroppe, L., Baccalá, L. A., & Sameshima, K. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091466 · Cited by 5
Advanced methods for time-varying effective connectivity estimation in memory processes
Laura Astolfi, A., Toppi, J., Wood, G. M. D. O., Kober, S. E., Risetti, M., Macchiusi, L., Salinari, S., Babiloni, F., & Donatella Mattia, A. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610155 · Cited by 5
Kurgansky, А. V., & Grigal, P. P. (2010) · Human Physiology · doi:10.1134/s0362119710040055 · Cited by 5
Comparison of methods for estimation of time-varying transmission in multichannel data
Kamiński, M., Szerling, P., & Blinowska, K. J. (2010) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687719 · Cited by 5
State-Space Multivariate Autoregressive Models for Estimation of Cortical Connectivity from EEG
Cheung, B. L. P., Riedner, B. A., Tononi, G., & Veen, B. D. V. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5335049 · Cited by 5
Truccolo, W., Ding, M., & Bressler, S. L. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00433-7 · Cited by 5
Self-citationYuan, Z. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2033212 · Cited by 4
Li, Z., Tong, L., Zeng, Y., Pei, C., & Yan, B. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf048 · Cited by 5
Hamedi, M., Salleh, S., Ting, C., Samdin, S. B., & Noor, A. M. (2015) · International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) · doi:10.1109/icbaps.2015.7292230 · Cited by 4
The Graph Theoretical Approach in Brain Functional Networks
Fallani, F. D. V., & Babiloni, F. (2010) · Synthesis lectures on biomedical engineering · doi:10.2200/s00279ed1v01y201004bme036 · Cited by 6
Functional brain network analysis reveals time-on-task related performance decline
Sun, Y., Bezerianos, A., Thakor, N. V., & Li, J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512265 · Cited by 4
García‐García, R., Guerrero, J., Lavilla‐Miyasato, M., Magdalena‐Benedito, R., Ordoño, J. F., Llansola, M., Montoliu, C., Teruel‐Martí, V., & Felipo, V. (2019) · Journal of Neurochemistry · doi:10.1111/jnc.14941 · Cited by 4
Protopapa, F., Mylonas, D., Spiliotis, K., Siettos, C. I., Smyrnis, N., & Evdokimidis, I. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091458 · Cited by 4
Wang, H., Wu, X., Wen, X., Lei, X., Gao, Y., & Yao, L. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.02.016 · Cited by 4
Top-Down Cortical Influences in Visual Expectation
Bressler, S. L., Richter, C. G., Chen, Y., & Ding, M. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.246707 · Cited by 4
Self-citationAdaptive parametric spectral estimation with Kalman smoothing for online early seizure detection
Park, Y. S., Hochberg, L. R., Eskandar, E. N., Cash, S. S., & Truccolo, W. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6696207 · Cited by 4
The causal interaction within attention networks and emotion network: A fMRI study
Liu, S., Kong, X., Jin, Z., & Li, L. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944102 · Cited by 4
Sobolewski, A., Kublik, E., Świejkowski, D. A., Łęski, S., Kamiński, J., & Wróbel, A. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0220-0 · Cited by 4
Dynamic autoregressive neuromagnetic causality imaging (DANCI)
Frye, R. E., Wu, M., & Zouridakis, G. (2007) · Cited by 4
Liu, Y., & Liang, C. (2020) · International Journal of Educational Psychology · doi:10.17583/ijep.2020.3940 · Cited by 5
Análisis matemáticos en el estudio de señales electroencefalográficas
Bolumburu, E. U., & Franco, J. I. (2005) · Revista de Neurología · doi:10.33588/rn.4107.2004577 · Cited by 4
Neural Mechanisms of Attentional Control
Yantis, S. (2002) · Springer eBooks · doi:10.1007/978-1-4615-0111-4_14 · Cited by 4
Frye, R. E. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.10.006 · Cited by 4
Liang, H., Ding, M., & Bressler, S. L. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00515-x · Cited by 4
Self-citationSensorimotor cortical-subthalamic network dynamics during force generationPreprint
Alhourani, A., Korzeniewska, A., Wozny, T. A., Lipski, W. J., Kondylis, E. D., Ghuman, A. S., Crone, N. E., Crammond, D. J., Turner, R. S., & Richardson, R. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/306332 · Cited by 4
Fundamentals of EEG Signal Processing
Sanei, S., & Chambers, J. A. (2021) · Wiley eBooks · doi:10.1002/9781119386957.ch4 · Cited by 4
Revealing Whole-Brain Causality Networks During Guided Visual Searching
Kiefer, C. M., Ito, J., Weidner, R., Boers, F., Shah, N. J., Grün, S., & Dammers, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.826083 · Cited by 3
Li, J., Chan, S. C., Jiang, Y., & Chai, B. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3216380 · Cited by 4
Causal interactions in brain networks predict pain levels in trigeminal neuralgia
Liang, Y., Zhao, Q., Neubert, J. K., & Ding, M. (2024) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2024.110947 · Cited by 3
Self-citationDual EEG alignment between participants during shared intentionality experiments
Myers, M. H., & Hossain, G. (2022) · Brain Research · doi:10.1016/j.brainres.2022.147986 · Cited by 3
Lee, G., Ro, D., Shin, S., & Kim, Y. (2023) · Neurophotonics · doi:10.1117/1.nph.10.2.025015 · Cited by 3
Semantic processing during continuous speech production: an analysis from eye movements and EEG
Huang, J., Zhang, G., Dang, J., Chen, Y., & Miyamoto, S. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1253211 · Cited by 3
Zhang, Y., Zhu, H., & Franz, E. A. (2024) · Experimental Brain Research · doi:10.1007/s00221-024-06948-y · Cited by 5
EEG-fMRI measures of functional brain connectivity in epilepsy
Murta, T., Figueiredo, P., & Leal, A. (2011) · IEEE Portuguese Meeting on Bioengineering · doi:10.1109/enbeng.2011.6026094 · Cited by 3
Connectivity based feature-level filtering for single-trial EEG BCIs
Heger, D., Terziyska, E., & Schultz, T. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853962 · Cited by 3
Blinowska, K. J., Kamiński, M., Kamiński, J., & Brzezicka, A. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626857 · Cited by 3
Resting-state brain networks revealed by granger causal connectivity in frogs
Xue, F., Fang, G., Yue, X., Zhao, E., Brauth, S. E., & Tang, Y. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.08.015 · Cited by 3
A lateralized functional auditory network is involved in anuran sexual selection
Xue, F., Fang, G., Yue, X., Zhao, E., Brauth, S. E., & Tang, Y. (2016) · Journal of Biosciences · doi:10.1007/s12038-016-9638-5 · Cited by 4
On the tracking of dynamic functional relations in monkey cerebral cortex
Liang, H., Ding, M., & Bressler, S. L. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00257-5 · Cited by 3
Self-citationRegulation of p53 expression in HeLa cells.
Chen, Z. P., & Yeung, D. C. M. (1996) · PubMed · Cited by 3
Statistical Analysis of EEG Phase Shift Events
Marshall, W. (2014) · UWSpace (University of Waterloo) · Cited by 3
Heartfelt Face Perception via the Interoceptive Pathway – an MEG studyPreprint
Kim, J., Park, H.-D., Kim, K. W., Shin, D. W., Lim, S., Kwon, H., Kim, M., Kim, K., & Jeong, B. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/200980 · Cited by 3
Global disinhibition and corticospinal plasticity for drastic recovery after spinal cord injuryPreprint
Yamaguchi, R., Ueno, S., Kawasaki, T., Chao, Z. C., Mitsuhashi, M., Isa, K., Takei, T., Kobayashi, K., Takahashi, J. A., Onoe, H., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.15.532773 · Cited by 3
TMS-evoked potential propagation reflects effective brain connectivity
Daly, I. P., Williams, N., & Nasuto, S. J. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad9ee0 · Cited by 2
Application of Time-varying Coherence to Coordinative Connectivity Based on Event Related EEG
Wen, X., Yin, K., Sun, D., Yao, L., & Zhao, X. (2007) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2007.265 · Cited by 2
Directed Interregional Brain Interactions
Bressler, S. L. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_4 · Cited by 2
Self-citationInformation flow and children’s emotions during collaborative coding: A causal analysis
Sharma, K., Papavlasopoulou, S., Lee‐Cultura, S., & Giannakos, M. N. (2021) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3459990.3460731 · Cited by 2
Usami, K., Matsumoto, R., Korzeniewska, A., Shimotake, A., Matsuhashi, M., Nakae, T., Kikuchi, T., Yoshida, K., Kunieda, T., Takahashi, R., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac456 · Cited by 2
Wang, W., & Sun, W. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140990 · Cited by 2
Effective brain connectivity related to non-painful thermal stimuli using EEG
Cuevas, D. C. S., Ruiz, R. E. C., Collina, D. D., & Tierra-Criollo, C. J. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad53ce · Cited by 2
Asadpour, A., & Wong‐Lin, K. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-78857-4_14 · Cited by 2
Darvishi-Bayazi, M.-J., Nasrabadi, A. M., & Curran, T. (2016) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme.2016.7890930 · Cited by 2
Estimation of Cortical Connectivity in Humans: Advanced Signal Processing Techniques
Astolfi, L., & Babiloni, F. (2007) · Synthesis lectures on biomedical engineering · doi:10.2200/s00094ed1v01y200708bme013 · Cited by 2
Identification of stock market forces in the system adaptation framework
Zheng, X., & Chen, B. M. (2011) · International Conference on Control and Automation · doi:10.1109/icca.2011.6138027 · Cited by 2
Cortical functional connectivity under different auditory attentional efforts
Hong, X., & Tong, S. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347409 · Cited by 2
Griffiths, J. D. (2015) · Physics of Life Reviews · doi:10.1016/j.plrev.2015.11.003 · Cited by 2
Hettiarachchi, I. T., Mohamed, S. M. K., Nahavandi, S., & Nahavandi, S. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.323 · Cited by 2
Signal Modelling for the Digital Reconstruction of Gramophone Noise
Stallmann, C. F., & Engelbrecht, A. P. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-30222-5_19 · Cited by 2
Emergence of the Affect from the Variation in the Whole-Brain Flow of Information
Keshmiri, S., Shiomi, M., & Ishiguro, H. (2019) · Brain Sciences · doi:10.3390/brainsci10010008 · Cited by 2
State Space Methods for Granger-Geweke Causality MeasuresPreprint
Solo, V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04663 · Cited by 2
Wang, S. (2005) · IEE International Seminar on Medical Applications of Signal Processing · doi:10.1049/ic:20050339 · Cited by 2
Glickman, M., & Tang, A. C. (2009) · doi:10.2172/978914 · Cited by 2
Functional and effective connectivity in MEG. Application to the study of epilepsy
Niso, G. (2013) · doi:10.20868/upm.thesis.23086 · Cited by 1
Metadata
- Authors
- Ding, M., Bressler, S. L., Yang, W., & Liang, H.
- Affiliations
- Florida Atlantic University (US)
- Venue
- Biological Cybernetics
- Published
- 2000-06-21
- Volume
- 83
- Issue
- 1
- Pages
- 35-45
- ISSN
- 0340-1200 (ISSN-L)
- 1432-0770
- OpenAlex
- W2074501647
- Cited by
- 582
- References
- 16
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 12 · 2023: 19 · 2022: 24 · 2021: 20 · 2020: 25 · 2019: 36 · 2018: 32 · 2017: 22 · 2016: 32 · 2015: 31 · 2014: 37 · 2013: 36 · 2012: 33
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Biological Cybernetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r03mh058190-02)
- National Institute of Mental Health (MH-58190-02)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- event-related potentials, parametric spectral estimation, non-stationary time series, data preprocessing, model validation, cortical dynamics
- MeSH terms
- Animals, Cerebral Cortex, Cognition, Evoked Potentials, Macaca, Models, Neurological, Pattern Recognition, Visual, Psychomotor Performance, Reproducibility of Results, Multivariate Analysis
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