The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects
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Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
Gait Analysis: Normal and Pathological Function
(1992) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.1992.03490220101046 · Cited by 3,875
Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants
BOHANNON, R. W. (1997) · Age and Ageing · doi:10.1093/ageing/26.1.15 · Cited by 2,341
The five percent electrode system for high-resolution EEG and ERP measurements
Oostenveld, R., & Praamstra, P. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(00)00527-7 · Cited by 1,909
Learning Overcomplete Representations
Lewicki, M. S., & Sejnowski, T. J. (2000) · Neural Computation · doi:10.1162/089976600300015826 · Cited by 1,129
Dynamic Sensorimotor Interactions in Locomotion
Rossignol, S., Dubuc, R., & Gossard, J.-P. (2006) · Physiological Reviews · doi:10.1152/physrev.00028.2005 · Cited by 1,096
Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones
Makeig, S. (1993) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(93)90110-h · Cited by 844
Cited by
Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758
Luu, T. P., Nakagome, S., He, Y., & Contreras-Vidal, J. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-09187-0 · Cited by 101
Prediction of gait intention from pre-movement EEG signals: a feasibility study
Shafiul Hasan, S. M., Siddiquee, M. R., Atri, R., Ramon, R., Marquez, J. S., & Bai, O. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-020-00675-5 · Cited by 63
EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review
(2022) · doi:10.3390/neurolint14040084 · Cited by 28
Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform
Hasan, S. M. S., Siddiquee, M. R., & Bai, O. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2998778 · Cited by 22
Metadata
- Authors
- Wagner, J., Solis-Escalante, T., Grieshofer, P., Neuper, C., Müller-Putz, G., & Scherer, R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 63
- Issue
- 3
- Pages
- 1203-1211
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2044806915
- Cited by
- 301
- References
- 61
- FWCI
- 8.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. https://app.cochat.ai/papers/10.1016/j.neuroimage.2012.08.019
- The paper
- Wagner, J., Solis-Escalante, T., Grieshofer, P., Neuper, C., Müller-Putz, G., & Scherer, R. (2012). Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects. NeuroImage. https://doi.org/10.1016/j.neuroimage.2012.08.019
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.
