A Lasso quantile periodogram based feature extraction for EEG-based motor imagery

摘要

Background: The extraction of relevant and distinct features from the electroencephalogram (EEG) signals is one of the most challenging task when implementing Brain Computer Interface (BCI) based systems. Frequency analysis techniques are recognised as one of the most suitable methods to have distinct information from EEG signals. However, existing studies use mostly classical approaches assuming that the signal is Gaussian, stationary and linear. These properties are not verified in the EEG case considering the complexity of the brain electrical activity.