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Interpretable deep neural networks for single-trial EEG classification
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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
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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
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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
Deep learning-based electroencephalography analysis: a systematic review
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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477
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Unmasking Clever Hans predictors and assessing what machines really learn
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“The only exceptions are [39, 113] that using EEG data provide heatmaps with data point’s relevance for the decision’s outcome.”
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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
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