The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
Deep learning based on Batch Normalization for P300 signal detection
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This approach has been shown to achieve good classification accuracy.”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [13]
In the citing paper
“To improve SNR, repeated stimuli are used and multiple trials of EEG collections are averaged [13].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [13]
In the citing paper
“BN3 [13] is a deep learning model specifically designed for P300 detection, which uses Batch Normalization for EEG signals and a conventional CNN model for classification.”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [13]
In the citing paper
“they learn spatial features (i.e., spatial convolution, performed across electrodes) and then temporal features in the next layers (i.e., temporal convolution, performed across time samples)”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Liu et al., 2018
In the citing paper
“they do not address the challenge of reducing the number of trainable parameters.”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Liu et al., 2018
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
(2014) · Cited by 33,419
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475
Cited by
Sorted by citations
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
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition
Zhang, D., Yao, L., Chen, K., Wang, S., Chang, X., & Liu, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2905157 · Cited by 367
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296
Zang, H., Cheng, L., Ding, T., Cheung, K. W., Wei, Z., & Sun, G. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105790 · Cited by 282
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taïar, R., Hancock, P. A., & Aljuaid, A. M. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 295
Metadata
- Authors
- Liu, M., Wu, W., Gu, Z., Yu, Z., Qi, F., & Li, Y.
- Affiliations
- South China University of Technology (CN)
- Corresponding author
- Wu, Wei
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 275
- Pages
- 288-297
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2751159520
- Cited by
- 230
- References
- 30
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 29 · 2023: 31 · 2022: 29 · 2021: 51 · 2020: 36 · 2019: 29 · 2018: 11
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61403144, 61573152, 61573150, 61633010, 91420302)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- batch normalization, convolutional neural networks, brain-computer interface, P300 speller, oddball paradigm, character recognition, overfitting, rectified linear units, EEG
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 ›
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