A data augmentation scheme for geometric deep learning in personalized brain–computer interfaces.
Open accessAbstract
Electroencephalography signals inherently deviate from the notion of regular spatial sampling, as they reflect the coordinated action from multiple distributed overlapping cortical networks. Hence, the observed brain dynamics are influenced both by the topology of the sensor array and the underlying functional connectivity. Neural engineers are currently exploiting the advances in the domain of graph signal processing in an attempt to create robust and reliable brain decoding systems. In this direction, Geometric Deep Learning is a highly promising concept for combining the benefits of graph signal processing and deep learning towards revolutionising Brain-Computer Interfaces (BCIs). However, its exploitation has been hindered by its data-demanding character. As a remedy, we propose here a novel data augmentation approach that combines the multiplex network modelling of multichannel signal with a graph variant of the classical Empirical Mode Decomposition (EMD), and which proves to be a strong asset when combined with Graph Convolutional Neural Networks (GCNNs). As our graph-EMD algorithm makes no assumptions with respect to linearity and stationarity, it appears as an appealing solution towards analysing brain signals without artificially imposing regularities in either temporal or spatial domain. Our experimental results indicate that the proposed scheme for data augmentation leads to substantial improvement when it is combined with GCNNs. Using recordings from two distinct BCI applications and comparing against a state-of-the-art augmentation method, we illustrate the benefits from its use. By making it available to BCI community, we hope to further foster the application of geometric deep learning in the field.
Integrity signals
Registry
Cites
Statistics
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?
Integrating graph CNN with DA obtained more stable performance in a sustained attention driving task.
“The experimental results show that the exploration of the graph structure of EEG signal could reflect the spatial feature of signal and the methodology of integrating graph CNN with DA has obtained a more stable performance.”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Kalaganis et al., 2020
Cites
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 6,912 · see citing works
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., & Vandergheynst, P. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2235192 · Cited by 4,421 · see citing works
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works
Alpha-band oscillations, attention, and controlled access to stored information
Klimesch, W. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.10.007 · Cited by 3,310 · see citing works
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125 · see citing works
The structure and dynamics of multilayer networksPreprint
Wangk, Z., & Zaninm, M. (2016) · Cited by 3,046 · see citing works
Wavelets on graphs via spectral graph theory
Hammond, D. K., Vandergheynst, P., & Gribonval, R. (2010) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.04.005 · Cited by 2,173 · see citing works
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 · see citing works
Foxe, J. J., & Snyder, A. C. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00154 · Cited by 1,558 · see citing works
On empirical mode decomposition and its algorithms
Rilling, G., Flandrin, P., & Gonçalvès, P. (2003) · Cited by 1,402 · see citing works
Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2930186 · Cited by 545 · see citing works
Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors
Liu, N.-H., Chiang, C.-Y., & Chu, H.-C. (2013) · Sensors · doi:10.3390/s130810273 · Cited by 412 · see citing works
Multi-channel EEG recordings during a sustained-attention driving task
Cao, Z., Chuang, C.-H., King, J.-K., & Lin, C.-T. (2019) · Scientific Data · doi:10.1038/s41597-019-0027-4 · Cited by 289 · see citing works
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsPreprint
Hartmann, K. G., Schirrmeister, R. T., & Tonio Ball, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.01875 · Cited by 204 · see citing works
Wang, G., CHEN, X.-Y., Qiao, F., Wu, Z., & Huang, N. E. (2010) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536910000549 · Cited by 201 · see citing works
A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals
Zhang, Z., Duan, F., Solé‐Casals, J., Dinarès-Ferran, J., Cichocki, A. S., Yang, Z., & Sun, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2895133 · Cited by 193 · see citing works
Grassi, F., Loukas, A., Perraudin, N., & Ricaud, B. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2775589 · Cited by 174 · see citing works
Mapping Multiplex Hubs in Human Functional Brain Networks
De Domenico, M., Sasai, S., & Arenas, A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00326 · Cited by 144 · see citing works
The TUH EEG CORPUS: A big data resource for automated EEG interpretation
Harati, A., Lopez, S. M., Iyad Obeid, A., Picone, J., Jacobson, M. P., & Tobochnik, S. D. (2014) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb.2014.7002953 · Cited by 141 · see citing works
Kumar, S., Sharma, A. K., & Tsunoda, T. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1964-6 · Cited by 141 · see citing works
Bayesian Machine Learning: EEG\/MEG signal processing measurements
Wu, W., Nagarajan, S. S., & Chen, Z. S. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2481559 · Cited by 116 · see citing works
Brain–computer interface: Changes in performance using virtual reality techniques
Ron-Angevín, R., & Estrella, A. D. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2008.10.099 · Cited by 110 · see citing works
Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings
Inuso, G., Foresta, F. L., Mammone, N., & Morabito, F. C. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371184 · Cited by 104 · see citing works
Multi-channel EEG recordings during a sustained-attention driving task
Cao, Z., Chuang, C., King, J.-K., & Lin, C. (2018) · Figshare · doi:10.48550/arxiv.1809.06534 · Cited by 96 · see citing works
Dinarès-Ferran, J., Ortner, R., Guger, C., & Solé‐Casals, J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00308 · Cited by 57 · see citing works
Network neuroscience for optimizing brain–computer interfaces
Fallani, F. D. V., & Bassett, D. S. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.10.001 · Cited by 47 · see citing works
Dimensionality reduction of brain imaging data using graph signal processing
Liu, R., Nejati, H., & Cheung, N. M. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532574 · Cited by 43 · see citing works
Kalaganis, F. P., Laskaris, N., Chatzilari, E., Nikolopoulos, S., & Kompatsiaris, I. Y. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2912066 · Cited by 31 · see citing works
Self-citationGenerating Artificial EEG Signals To Reduce BCI Calibration Time
Lotte, F. (2011) · Cited by 30 · see citing works
Nguyen, C. H., Karavas, G. K., & Artemiadis, P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212620 · Cited by 30 · see citing works
Connectivity steered graph Fourier transform for motor imagery BCI decoding
Georgiadis, K., Laskaris, N., Nikolopoulos, S., & Kompatsiaris, I. Y. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab21fd · Cited by 26 · see citing works
Self-citationAnisotropic Interpolation on Graphs: The Combinatorial Dirichlet Problem
Grady, L. J., & Schwartz, E. L. (2003) · Cited by 24 · see citing works
Paris, A., Atia, G., Vosoughi, A., & Berman, S. A. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2606595 · Cited by 20 · see citing works
Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics
Buch, V., Richardson, A. G., Brandon, C., Stiso, J., Khattak, M. N., Bassett, D. S., & Lucas, T. H. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00790 · Cited by 20 · see citing works
Liparas, D., Dimitriadis, S. I., Laskaris, N., Tzelepi, A., Charalambous, K., & Angelis, L. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2014.04.032 · Cited by 17 · see citing works
Self-citationGraph Empirical Mode Decomposition
Borgnat, P., Flandrin, P., & Tremblay, N. (2014) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.43868 · Cited by 15 · see citing works
A brain signals-based interface between drivers and in-vehicle devices
He, T., Bi, L., Lian, J., & Sun, H. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535563 · Cited by 6 · see citing works
A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry
Kalaganis, F. P., Laskaris, N., Chatzilari, E., Adamos, D. A., Nikolopoulos, S., & Kompatsiaris, I. Y. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8130 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review
He, C., Liu, J., Zhu, Y., & Du, W. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.765525 · Cited by 105 · see citing works
““Neonatal seizure detection using convolutional neural networks,” in Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP, Tokyo. doi: 10.1109/MLSP.2017.8168193 Panwar, S., Rad, P., Jung, T.-P., and Huang, Y. (2020).”
MethodInfluentialKo, W., Jeon, E., Jeong, S., Phyo, J., & Suk, H. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.643386 · Cited by 58 · see citing works
Personalized Brain–Computer Interface and Its Applications
Ma, Y., Gong, A., Nan, W., Ding, P., Wang, F., & Fu, Y. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010046 · Cited by 49 · see citing works
Dawsari, H. A., Al-Ahmadi, S. A., & Muhammad, F. N. (2023) · Diagnostics · doi:10.3390/diagnostics13162624 · Cited by 32 · see citing works
Ahuja, C. K., & Sethia, D. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1421922 · Cited by 31 · see citing works
A new data augmentation method for EEG features based on the hybrid model of broad-deep networks
Fu, R., Wang, Y., & Jia, C. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117386 · Cited by 29 · see citing works
Emotion Recognition from Physiological Channels Using Graph Neural Network
Wierciński, T., Rock, M., Zwierzycki, R., Zawadzka, T., & Zawadzki, M. (2022) · Sensors · doi:10.3390/s22082980 · Cited by 26 · see citing works
EEG-based Classification of Drivers Attention using Convolutional Neural Network
Atilla, F., & Alimardani, M. (2021) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms53169.2021.9582625 · Cited by 22 · see citing works
Lee, S., Jang, S., & Jun, S. C. (2022) · Electronics · doi:10.3390/electronics11172706 · Cited by 16 · see citing works
A State‐of‐the‐Art Computer Vision Adopting Non‐Euclidean Deep‐Learning Models
Chowdhury, S. R., Sany, M. R., Ahamed, M. H., Das, S. K., Badal, F. R., Das, P., Tasneem, Z., Hasan, M. M., Islam, M. R., Ali, M. F., et al. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8674641 · Cited by 13 · see citing works
Li, Y., Yang, Y., Song, S., Wang, H., Sun, M., Liang, X., Zhao, P., Wang, B., Wang, N., Sun, Q., et al. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1439607 · Cited by 12 · see citing works
Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry
Georgiadis, K., Kalaganis, F. P., Oikonomou, V. P., Nikolopoulos, S., Laskaris, N., & Kompatsiaris, I. Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43075-6_3 · Cited by 11 · see citing works
Self-citationA causal perspective on brainwave modeling for brain–computer interfaces
Barmpas, K., Panagakis, Y., Zoumpourlis, G., Adamos, D. A., Laskaris, N., & Zafeiriou, S. P. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad3eb5 · Cited by 10 · see citing works
Self-citationOn Using AI for EEG-Based BCI Applications: Problems, Current Challenges and Future Trends
Barbera, T., Burger, J., D’Amelio, A., Zini, S., Bianco, S., Lanzarotti, R., Napoletano, P., Boccignone, G., & Contreras-Vidal, J. L. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2561185 · Cited by 9 · see citing works
A hybrid neuromarketing approach exploiting EEG graph signal processing and gaze dynamic patterning
Kalaganis, F. P., Georgiadis, K., Oikonomou, V. P., Laskaris, N. A., Nikolopoulos, S., & Kompatsiaris, I. Y. (2025) · Brain Informatics · doi:10.1186/s40708-025-00272-z · Cited by 7 · see citing works
Self-citationIs that real? A multifaceted evaluation of the quality of simulated EEG signals for passive BCI
Eilts, H., & Putze, F. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945093 · Cited by 5 · see citing works
Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network
Ghosh, S., Biswas, D., Rohan, N., Vijayan, S., & Chakravarthy, V. S. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1513374 · Cited by 5 · see citing works
Kołodziej, M., Majkowski, A., Rak, R. J., & Wiszniewski, P. (2023) · Applied Sciences · doi:10.3390/app132413350 · Cited by 3 · see citing works
Liu, C., Raza, H., & Bhattacharyya, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85955-4.00010-7 · Cited by 2 · see citing works
Graph Theory for Brain Signal Processing
Laskaris, N., Adamos, D. A., & Bezerianos, A. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_66-2 · Cited by 1 · see citing works
Self-citationGraph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis
Chen, X., Li, B., Jia, H., Feng, F., Duan, F., Sun, Z., Caiafa, C. F., & Solé‐Casals, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.866735 · Cited by 1 · see citing works
Targeted deep learning: Framework, methods, and applications
Huang, S., & Lederer, J. (2023) · Stat · doi:10.1002/sta4.556 · Cited by 1 · see citing works
Graph Theory for Brain Signal Processing
Laskaris, N., Adamos, D. A., & Bezerianos, A. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_66 · Cited by 1 · see citing works
Self-citationUser-Centered Design of Neuroprosthetics: Advancements and Limitations
Badr, Y., AlSawaftah, N., & Husseini, G. A. (2025) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/0118715273335487250102093150
A Review on Brain-Computer Interface
Rakesh, S., Shetkar, A., Gayatri, P. U., & Reddy, P. (2024) · Intelligent Systems and Machine Learning Conference (ISML) · doi:10.1109/isml60050.2024.11007402
BGTransform: a neurophysiologically informed EEG data augmentation framework
Yue, J., Xiao, X., Zhang, H., Xu, M., & Ming, D. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae0c3a
- Authors
- Kalaganis, F. P., Laskaris, N. A., Chatzilari, E., Nikolopoulos, S., & Kompatsiaris, I.
- Affiliations
- Aristotle University of Thessaloniki (GR)
- Information Technologies Institute (GR)
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 162218-162229
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3083221507
- Cited by
- 26
- References
- 39
- Citations by year
- 2025: 5 · 2024: 4 · 2023: 7 · 2022: 6 · 2021: 4
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 Framework Programme (644780)
- European Union and Greek national funds through the Operational Program Competitiveness Entrepreneurship and Innovation (T1EDK-02454)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- data augmentation, geometric deep learning, brain-computer interface, EEG, graph convolutional networks, multiplex networks, graph signal processing, EEG signal analysis, brain decoding
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
