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Unsupervised cross-subject BCI learning and classification using riemannian geometry

Nasiri Ghosheh Bolagh, S ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. Publisher: i6doc.com publication , 2016
  3. Abstract:
  4. 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-subject learning without the need of any labeled data from test subjects
  5. Keywords:
  6. Artificial intelligence ; Geometry ; Interfaces (computer) ; Learning systems ; Neural networks ; Classification framework ; Labeled data ; New approaches ; Riemannian geometry ; Selection algorithm ; Brain computer interface
  7. Source: 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016, 27 April 2016 through 29 April 2016 ; 2016 , Pages 307-312 ; 9782875870278 (ISBN)