The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.
Advances in Quantitative Electroencephalogram Analysis Methods
Abstract
▪ Abstract Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 36,808
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
Possible generalization of Boltzmann-Gibbs statistics
Tsallis, C. (1988) · Journal of Statistical Physics · doi:10.1007/bf01016429 · Cited by 9,589
Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
Measuring the strangeness of strange attractors
Grassberger, P., & Procaccia, I. (1983) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(83)90298-1 · Cited by 5,650
Characterization of Strange Attractors
Grassberger, P., & Procaccia, I. (1983) · Physical Review Letters · doi:10.1103/physrevlett.50.346 · Cited by 4,857
Spectrum estimation and harmonic analysis
Thomson, D. J. (1982) · Proceedings of the IEEE · doi:10.1109/proc.1982.12433 · Cited by 4,418
Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · Archiv für Psychiatrie und Nervenkrankheiten · doi:10.1007/bf01797193 · Cited by 4,235
Measuring phase synchrony in brain signals
Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c · Cited by 3,798
Cited by
Estimation of entropy and mutual information
Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
Nonlinear multivariate analysis of neurophysiological signals
Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2005.10.003 · Cited by 1,076
Frontal-midline theta from the perspective of hippocampal “theta”
Mitchell, D. J., McNaughton, N., Flanagan, D., & Kirk, I. J. (2008) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2008.09.005 · Cited by 549
ACHARYA, U. R., SREE, S. V., ANG, P. C. A., YANTI, R., & SURI, J. S. (2012) · International Journal of Neural Systems · doi:10.1142/s0129065712500025 · Cited by 320
A method of identifying chronic stress by EEG
Peng, H., Hu, B., Zheng, F., Fan, D., Zhao, W., Chen, X., Yang, Y., & Cai, Q. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-012-0593-3 · Cited by 55
Metadata
- Authors
- Thakor, N. V., & Tong, S.
- Venue
- Annual Review of Biomedical Engineering
- Publisher
- Annual Reviews
- Published
- 2004-08-15
- Volume
- 6
- Issue
- 1
- Pages
- 453-495
- ISSN
- 1523-9829 (ISSN-L)
- 1545-4274
- OpenAlex
- W2136117227
- Cited by
- 305
- References
- 112
- FWCI
- 0.87
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
