PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating Noise and Dimensionality in BCI Data Sets: Towards BCI Illiteracy Comprehension
Claudia Sannelli, Mikio braun, Michael Tangermann and Klaus-Robert Müller
In: 4th International Brain-Computer Interface Workshop and Training Course 2008, 18-20 Sep 2008, Graz, Austria.


About one third of the BCI subjects cannot communicate via BCI, a phenomenon that is known as BCI illiteracy. New investigations aiming to an early prediction of illiteracy would be very helpful to understand this phenomenon and to avoid hard BCI training for many subjects. In this paper, the first application on to electroencephalogram (EEG) of a newly developed machine learning tool, Relevant Dimension Estimation (RDE), is presented. RDE is capable to estimate the intrinsic noise present in a data set and the dimensionality of the learning problem, extracting from the data the label relevant information. Applied to EEG data collected during motor imagery paradigms in many BCI sessions, RDE is able to deliver interesting insight into the illiteracy phenomenon. In particular, RDE can demonstrate that illiteracy is mostly not due to the non-stationarity or high dimensionality present in the data, but rather due to a high intrinsic noise in the label related information. Moreover, RDE allows to detect individual BCI-illiterate subjects from a small amount of example data in a very reliable way, based on a combination of the estimated noise, the dimensionality and the width of the chosen kernel.

EPrint Type:Conference or Workshop Item (Talk)
Additional Information:Won the young scientists best talk award.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:4708
Deposited By:Mikio braun
Deposited On:24 March 2009