Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
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In a CNN, the initial layer extracts local and spatial features while the end layer extracts global features exclusively.
“As the convolution process progresses layer by layer, the initial layer extracts local and spatial features while the end layer extracts global features exclusively [14].”
From Multimodal Emotion Recognition Using a Hierarchical Fusion Convolutional Neural Network (2021) · cites this paper as [14]
Cites
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 · see citing works
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 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
Deep 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 · see citing works
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511 · see citing works
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EEG-Based Pathology Detection for Home Health Monitoring
Muhammad, G., Hossain, M. S., & Kumar, N. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3020654 · Cited by 148 · see citing works
Self-citationCOVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images
Muhammad, G., & Shamim Hossain, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.013 · Cited by 120 · see citing works
Self-citationMultimodal Emotion Recognition Using a Hierarchical Fusion Convolutional Neural Network
Zhang, Y., Cheng, C., & Zhang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3049516 · Cited by 84 · see citing works
- Authors
- Amin, S. U., Alsulaiman, M., Muhammad, G., Mekhtiche, M. A., & Shamim Hossain, M.
- Affiliations
- King Saud University (SA)
- Corresponding author
- Muhammad, Ghulam
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 101
- Pages
- 542-554
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2954214015
- Cited by
- 523
- References
- 5
- Citations by year
- 2026: 37 · 2025: 68 · 2024: 91 · 2023: 103 · 2022: 103 · 2021: 91 · 2020: 28 · 2019: 2
- FWCI
- 32.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Future Generation Computer Systems (publishedVersion)
- Funders
- Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia (1436-023)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- EEG motor imagery classification, brain-computer interface, convolutional neural networks, cross-subject classification, spatiotemporal feature extraction, BCI Competition IV-2a
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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.
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