Online classification accuracy is a poor metric to study mental imagery-based bci user learning: an experimental demonstration and new metricsReportar como inadecuado




Online classification accuracy is a poor metric to study mental imagery-based bci user learning: an experimental demonstration and new metrics - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique 2 Potioc - Popular interaction with 3d content LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest 3 Hybrid - 3D interaction with virtual environments using body and mind Inria Rennes – Bretagne Atlantique , IRISA D6 - MEDIA ET INTERACTIONS

Abstract : While promising for many applications, Electroencephalography EEG-based Brain-Computer Interfaces BCIs are still scarcely used outside laboratories , due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires to use appropriate reliability metrics to quantify both signal processing and user learning performances. So far, Classification Accuracy CA is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study how well users are learning to use the BCI. Indeed CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful EEG pattern self-modulation by the user. We thus propose new performance metrics to specifically measure how distinct and stable the EEG patterns produced by the user are. By re-analyzing EEG data with these metrics, we indeed confirm that CA may hide some learning effects or hide the user inability to self-modulate a given EEG pattern.

Keywords : Brain-computer interfaces Performance Metric





Autor: Fabien Lotte - Camille Jeunet -

Fuente: https://hal.archives-ouvertes.fr/



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