The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Interpretable deep neural networks for single-trial EEG classification
Open accessOverview
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
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
Self-citationOptimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357
Self-citationEvaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164
Self-citation
Research works printed but not linked: 12
- Acqualagna, 2015, EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs), J. Neural Eng., 12, 026012, 10.1088/1741-2560/12/2/026012 · DOI
- Blankertz, 2006, The BCI competition III: validating alternative approaches to actual BCI problems, IEEE Trans. Neural Syst. Rehabil., 14, 153, 10.1109/TNSRE.2006.875642 · DOI · Paper page
- Blankertz, 2011, Single-trial analysis and classification of ERP components – a tutorial, NeuroImage, 56, 814, 10.1016/j.neuroimage.2010.06.048 · DOI · Paper page
- Delorme, 2014, Grand average ERP-image plotting and statistics: a method for comparing variability in event-related single-trial EEG activities across subjects and conditions, J. Neurosci. Methods, 250, 3, 10.1016/j.jneumeth.2014.10.003 · DOI
- Fazli, 2009, Subject-independent mental state classification in single trials, Neural Netw., 22, 1305, 10.1016/j.neunet.2009.06.003 · DOI
- Lapuschkin, 2016, The layer-wise relevance propagation toolbox for artificial neural networks, J. Mach. Learn. Res., 17, 1
- Montavon, 2013, Machine learning of molecular electronic properties in chemical compound space, New J. Phys., 15, 095003, 10.1088/1367-2630/15/9/095003 · DOI · Paper page
- Parra, 2005, Recipes for the linear analysis of EEG, NeuroImage, 28, 326, 10.1016/j.neuroimage.2005.05.032 · DOI · Paper page
- Plis, 2014, Deep learning for neuroimaging: a validation study, Front. Neurosci., 8
- Porbadnigk, 2013, Single-trial analysis of the neural correlates of speech quality perception, J. Neural Eng., 10, 056003, 10.1088/1741-2560/10/5/056003 · DOI
- Tzovara, 2012, The timing of exploratory decision-making revealed by single-trial topographic EEG analyses, NeuroImage, 60, 1959, 10.1016/j.neuroimage.2012.01.136 · DOI
- Yuksel, 2015, A neural network-based optimal spatial filter design method for motor imagery classification, PLOS ONE, 10, e0125039, 10.1371/journal.pone.0125039 · DOI
Cited by
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115
“For example, CNNs have been used for epilepsy prediction and monitoring [21–25], for auditory music retrieval [26, 27], for detection of visual-evoked responses [28–31] and for motor imagery classification [32, 33], while deep belief networks (DBNs) have been used for sleep stage detection [34], anomaly detection [35]…”
BackgroundMethodDeep learning with convolutional neural networks for EEG decoding and visualization
Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23730 · Cited by 3,751
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665
Self-citationA 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
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
Metadata
- Authors
- Sturm, I., Lapuschkin, S., Samek, W., & Müller, K.-R.
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 274
- Pages
- 141-145
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2344695102
- Cited by
- 374
- References
- 4
- FWCI
- 21.55
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- Brain Korea 21 Plus Program
- Deutsche Forschungsgemeinschaft
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: Interpretable deep neural networks for single-trial EEG classification. https://app.cochat.ai/papers/10.1016/j.jneumeth.2016.10.008
- The paper
- Sturm, I., Lapuschkin, S., Samek, W., & Müller, K.-R. (2016). Interpretable deep neural networks for single-trial EEG classification. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2016.10.008
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.
