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Communication Dans Un Congrès Année : 2013

Towards a general architecture for a co-learning of brain computer interfaces

Résumé

In this article we propose a software architecture for asynchronous BCIs based on co-learning, where both the system and the user jointly learn by providing feedback to one another. We propose the use of recent filtering techniques such as Riemann Geometry and ICA followed by multiple classifications, by both incremental supervised classifiers and minimally supervised classifiers. The classifier outputs are then combined adaptively according to the feedback using recursive neural networks.
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Dates et versions

hal-00931105 , version 1 (14-01-2014)

Identifiants

  • HAL Id : hal-00931105 , version 1

Citer

Nataliya Kos'Myna, Franck Tarpin-Bernard, Bertrand Rivet. Towards a general architecture for a co-learning of brain computer interfaces. NER 2013 - 6th International IEEE/EMBS Conference on Neural Engineering, Nov 2013, San Diego, Californie, United States. pp.1054-1057. ⟨hal-00931105⟩
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