One-class classifier based on Riemannian Geometry Distances for Outlier Detection in Motor Imagery*

Abstract

The classification of motor imagery in non-invasive brain-computer interface (BCI) is a challenge due to the high variation of brain evoked responses across users and the non-stationarity properties of the electroencephalography (EEG) signal. With different sessions from the same user, it is possible to find substantial differences that require the BCI system to be recalibrated. In clinical settings, it is therefore necessary to know when a system should be recalibrated or when the system should adapt itself to deal with the shifts in the signal, i.e., the covariate shift, and/or catch artefacts that deviate substantially from the original data distribution. In this paper, we propose to use density based one-class classifiers using distances based on the Riemannian geometry framework for assessing the distribution of the EEG signal in motor imagery BCI. We assess the performance of the algorithms with a database of 14 participants. The results show that sessions from the same person can be reliably detected using the proposed approach. We also assess how the one-class classifiers can be used to determine if it is necessary to run domain adaptation in the test phase. The results support the conclusion that the accuracy improves as the system is adapted to shifting domains in signals.