The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification
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Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Cognitive Load During Problem Solving: Effects on Learning
Sweller, J. (1988) · Cognitive Science · doi:10.1016/0364-0213(88)90023-7 · Cited by 11,048
Baddeley, A. D., & Hitch, G. (1974) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60452-1 · Cited by 9,860
The discovery of processing stages: Extensions of Donders' method
Sternberg, S. (1969) · Acta Psychologica · doi:10.1016/0001-6918(69)90055-9 · Cited by 3,137
Working memory, short-term memory, and general fluid intelligence: A latent-variable approach.
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.128.3.309 · Cited by 3,078
An automated version of the operation span task
Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005) · Behavior Research Methods · doi:10.3758/bf03192720 · Cited by 1,861
Conway, A. R. A., Cowan, N., Bunting, M. F., Therriault, D. J., & Minkoff, S. R. B. (2002) · Intelligence · doi:10.1016/s0160-2896(01)00096-4 · Cited by 1,108
The influence of leads on cognitive load and learning in a hypertext environment
Antonenko, P. D., & Niederhauser, D. S. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2009.10.014 · Cited by 154
Capturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation
Fairclough, S. H., Gilleade, K., Ewing, K. C., & Roberts, J. (2013) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2013.050694 · Cited by 60
Cited by
Yin, Z., Wang, Y., Liu, L., Zhang, W., & Zhang, J. (2017) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2017.00019 · Cited by 155
Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components
Xiao, X., Xu, M., Jin, J., Wang, Y., Jung, T.-P., & Ming, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2958641 · Cited by 113
Metadata
- Authors
- Baldwin, C. L., & Penaranda, B. N.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 59
- Issue
- 1
- Pages
- 48-56
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2080209737
- Cited by
- 159
- References
- 9
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification. https://app.cochat.ai/papers/10.1016/j.neuroimage.2011.07.047
- The paper
- Baldwin, C. L., & Penaranda, B. N. (2012). Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.07.047
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
