Evidence build-up facilitates on-line adaptivity in dynamic environments: example of the BCI P300-speller

Emmanuel Daucé 1 Eoin Thomas 2, 3
2 ATHENA - Computational Imaging of the Central Nervous System
CRISAM - Inria Sophia Antipolis - Méditerranée
3 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : We consider a P300 BCI application where the subjects can write figures and letters in an unsupervised fashion. We (i) show that a generic speller can attain the state-of-the-art accuracy without any training phase or calibration and (ii) present an adaptive setup that consistently increases the bit rate for most of the subjects.
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Emmanuel Daucé, Eoin Thomas. Evidence build-up facilitates on-line adaptivity in dynamic environments: example of the BCI P300-speller. 22nd European Symposium on Artificial Neural Networks, Apr 2014, Bruges, Belgium. ⟨hal-01104024⟩

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