Estimating Noise and Dimensionality in BCI Data Sets: Towards Illiteracy Comprehension

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. Detecting the label relevant information present in a data set, RDE estimates the intrinsic noise and the complexity of the learning problem. Applied to EEG data collected during motor imagery paradigms, RDE is able to deliver interesting insights 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, in this paper is shown how to detect individual BCI-illiterate subjects in a very reliable way, based on a combination of the several features extracted by RDE.