PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving BCI performance by task-related trial pruning
Claudia Sannelli, Mikio braun and Klaus-Robert Müller
Neural Networks Volume 22, Number 9, pp. 1295-1304, 2009. ISSN 0893-6080

Abstract

Noise in electroencephalography data (EEG) is an ubiquitous problem that limits the performance of brain computer interfaces (BCI). While typical EEG artifacts are usually removed by trial rejection or by filtering, noise induced in the data by the subject's failure to produce the required mental state is very harmful. Such "noise" effects are rather common, especially for naive subjects in their training phase and, thus, standard artifact removal methods would inevitably fail. In this paper, we present a novel method which aims to detect such defected trials taking into account the intended task by use of Relevant Dimensionality Estimation (RDE), a new machine learning method for denoising in feature space. In this manner, our method effectively "cleans" the training data and thus allows better BCI classification. Preliminary results conducted on a data set of 43 naive subjects show a significant improvement for 74% of the subjects.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6445
Deposited By:Mikio braun
Deposited On:08 March 2010