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Communication Dans Un Congrès Année : 2016

Unsupervised Cross-Subject BCI Learning and Classification using Riemannian Geometry

Résumé

The inter-subject variability poses a challenge in cross-subject Brain-Computer Interface learning and classification. As a matter of fact, in cross-subject learning not all available subjects may improve the performance on a test subject. In order to address this problem we propose a subject selection algorithm and we investigate the use of this algorithm in the Riemannian geometry classification framework. We demonstrate that this new approach can significantly improve cross-suject learning without the need of any labeled data from test subjects.

Domaines

Neurosciences
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Dates et versions

hal-01368298 , version 1 (19-09-2016)

Identifiants

  • HAL Id : hal-01368298 , version 1

Citer

Samaneh Nasiri Ghosheh Bolagh, Mohammad Bagher Shamsollahi, Christian Jutten, Marco Congedo. Unsupervised Cross-Subject BCI Learning and Classification using Riemannian Geometry. ESANN 2016 - 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, European Neural Network Society, Apr 2016, Bruges, Belgium. ⟨hal-01368298⟩
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