Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces
By Paolo Zanini, Marco Congedo, Christian Jutten, Salem Said, Yannick Berthoumieu. IEEE Transactions on Biomedical Engineering, 2018
347 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/c9338493d1cf475eed2df88b0bab5b24ced8c88e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Dongrui Wu
- He He
- Florian Yger
- Fabien Lotte
- Marco Congedo
- Andrzej S. Cichocki
- Alain Rakotomamonjy
- Laurent Bougrain
In Experimental and Cognitive Psychology
- Dongrui Wu
- Stephane T Aroca-Ouellette
- Frank Rudzicz
- Demetres Kostas
- Bin Hu
- Andrzej S. Cichocki
- Prayag Tiwari
- Yazhou Zhang
Citations in the corpus
- A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “These properties in part explain why Riemannian classification methods provide a good generalization capability [224, 238], which enabled researchers to set up calibration-free adaptive ERP-BCIs using simple subject-tosubject and session-to-session transfer learning strategies [6].”
- Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, neutral): “[57] proposed a Riemannian alignment (RA) approach to centre the EEG covariance matrices {C k } Nk n=1 in the k-th domain with respect to a reference covariance matrix Rk specific to that domain.”
- EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “One novel knowledge distillation DL framework for Affective BCI based on LSTM-Capsule structure that compresses the large model without performance degradation [198]. while significantly reducing the number of parameters to transfer.”
- Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, methodology): “Experiments have shown that RA-MDRM outperformed MDRM in MI and ERP tasks [43], when compared in a TL setting.”
- BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, methodology): “While this is typical, some procedures, like covariance-based Riemannian classification schemes, do not necessarily need different features for different tasks [2, 5].”