MOABB bibliography

To cite MOABB, you could use the following paper:

Vinay Jayaram and Alexandre Barachant. "MOABB: trustworthy algorithm benchmarking for BCIs." Journal of neural engineering 15.6 (2018): 066011. DOI

Papers using MOABB

To explore academic works that cite/use MOABB you can check out the Connected Papers Link

2023

• Dolzhikova, I., Abibullaev, B., & Zollanvari, A. (2023). A Jackknifed-inspired deep learning approach to subject-independent classification of EEG. Pattern Recognition Letters.
• Gwon, D., Won, K., Song, M., Nam, C. S., Jun, S. C., & Ahn, M. (2023). Review of public motor imagery and execution datasets in brain-computer interfaces. Frontiers in Human Neuroscience.
• Xu, D., Tang, F., Li, Y., Zhang, Q., & Feng, X. (2023). An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey. Brain Sciences, 13(3), 483. paper
• Deny, P., & Choi, K. W. (2023) A Study on Graph Transformer-based Network for Brain-Computer Interface. paper
• Carrara, I., & Papadopoulo, T. (2023). Classification of BCI-EEG based on augmented covariance matrix. arXiv.
• Andreev, A., & Cattan, G. (2023). Quantum Support Vector Machine Applied to the Classication of EEG Signals with Riemanian Geometry (HAL)[https://hal.science/hal-03939121/document]
• E. Fallenius, L. Karlsson, (2023). Tensor Decompositions of EEG Signals for Transfer Learning Applications, Tech Report
• Hu, H., Yue, K., Guo, M., Lu, K., & Liu, Y. (2023). Subject Separation Network for Reducing Calibration Time of MI-Based BCI. Brain Sciences, 13(2), 221.

2022

• Bleuzé, A., Mattout, J., & Congedo, M. (2022). Tangent space alignment: Transfer learning for Brain-Computer Interface. Frontiers in Human Neuroscience.

• Barthélemy, Q., Chevallier, S., Bertrand-Lalo, R., & Clisson, P. (2022). End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities. Brain-Computer Interfaces

• Kobler, R. J., Hirayama, J. I., Zhao, Q., & Kawanabe, M. (2022). SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. NeurIPS

• Fang, Y., Yap, P. T., Lin, W., Zhu, H., & Liu, M. (2022). Source-Free Unsupervised Domain Adaptation: A Survey. arXiv preprint.

• Wilson, D., Gemein, L. A. W., Schirrmeister, R. T., & Ball, T. (2022). Deep Riemannian Networks for EEG Decoding. arXiv preprint.

• Demir, A., Khalil, I., & Kiziltan, B. (2022). EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG. arXiv

• Chevallier, S., Corsi, M. C., Yger, F., & Fallani, F. D. V. (2022). Riemannian geometry for combining functional connectivity metrics and covariance in BCI. Software Impacts, 12, 100254.

• Corsi, M. C., Chevallier, S., Fallani, F. D. V., & Yger, F. (2022). Functional connectivity ensemble method to enhance BCI performance (FUCONE). IEEE Transactions on Biomedical Engineering.

• D. Kostas-Heliokinde (2022) On the difficulty of training deep neural networks with raw encephalography data. PhD

• X. Chen, X. Teng, H. Chen, Y. Pan, P. Geyer. (2022) Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX. arXiv

• Barthélemy, Q., Chevallier, S., Bertrand-Lalo, R., & Clisson, P. (2022). End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities. Brain Computer Interface journal

• Couvy-Duchesne, B., Bottani, S., Camenen, E., Fang, F., Fikere, M., Gonzalez-Astudillo, J., ... & Wright, M. (2022). Main existing datasets for open data research on humans. HAL

• P. Guetschel, T. Papadopoulo, M. Tangermann. Embedding neurophysiological signals. Proc. of the IEEE MetroXRAINE conference, Oct 2022, Roma, Italy. HAL

• J. Gonzalez-Astudillo. Development of Network Features for Brain-Computer Interfaces. PhD

• Zoumpourlis, G., & Patras, I. (2022). Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training. arXiv preprint arXiv:2211.11460.

2021

• Matthijs Pals, Rafael J. Pérez Belizón, Nicolas Berberich, Stefan K. Ehrlich, John Nassour & Gordon Cheng. "Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips", IEEE Engineering in Medicine & Biology Society (EMBC). DOI

2020

• Jan Sosulski, Jan-Philip Kemmer & Michael Tangermann Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis. Neuroinformatics (2020). DOI
• Xu, Jiachen, Moritz Grosse-Wentrup, and Vinay Jayaram. "Tangent space spatial filters for interpretable and efficient Riemannian classification." Journal of neural engineering 17.2 (2020): 026043. DOI
• Rodrigues, Pedro, Marco Congedo, and Christian Jutten. "Dimensionality transcending: a method for merging BCI datasets with different dimensionalities." IEEE Transactions on Biomedical Engineering (2020). DOI

2019

• Xu, Jiachen, Moritz Grosse-Wentrup, and Vinay Jayaram. "Interpretable Riemannian classification in brain-computer interfacing." (2019). DOI
• Xu, Jiachen, Vinay Jayaram, Bernhard Schölkopf, Moritz Grosse-Wentrup. "Feature extraction from the Hermitian manifold for Brain-Computer Interfaces." 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2019. DOI
• Congedo, Marco, Pedro Luiz Coelho Rodrigues, and Christian Jutten. "The Riemannian minimum distance to means field classifier." 8th Graz Brain-Computer Interface Conference 2019. 2019. DOI

2018

• Rodrigues, Pedro Luiz Coelho, Christian Jutten, and Marco Congedo. "Riemannian procrustes analysis: Transfer learning for brain–computer interfaces." IEEE Transactions on Biomedical Engineering 66.8 (2018): 2390-2401. DOI
• Vinay Jayaram and Alexandre Barachant. "MOABB: trustworthy algorithm benchmarking for BCIs." Journal of neural engineering 15.6 (2018): 066011. DOI