The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG
Open accessAbstract
This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.
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.
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
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Schelter, B., Timmer, J., & Eichler, M. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2009.01.006 · Cited by 214
Cited by
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Automagic: Standardized preprocessing of big EEG data
Pedroni, A., Bahreini, A., & Langer, N. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.046 · Cited by 292
Luu, T. P., Nakagome, S., He, Y., & Contreras-Vidal, J. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-09187-0 · Cited by 101
Chuang, C.-H., Chang, K.-Y., Huang, C.-S., & Jung, T.-P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119586 · Cited by 88
Self-citationPrediction 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
Metadata
- Authors
- Mullen, T., Kothe, C., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., & Jung, T.-P.
- Venue
- 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2013-07
- Volume
- 2013
- Pages
- 2184-2187
- OpenAlex
- W2020346648
- Cited by
- 390
- References
- 2
- FWCI
- 23.81
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. https://app.cochat.ai/papers/10.1109/embc.2013.6609968
- The paper
- Mullen, T., Kothe, C., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., & Jung, T.-P. (2013). Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc.2013.6609968
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.