Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution

TL;DR

This paper introduces a statistical model based on the generalized extreme value distribution to detect mu-suppression in EEG signals, enabling accurate classification of motor imagery, movement, and resting states in brain-computer interfaces.

Contribution

It proposes a novel application of the GEV distribution for EEG power spectrum modeling and a linear classifier for mu-suppression detection in BCI systems.

Findings

High classification accuracy achieved

Effective detection of mu-suppression

Distinguishes EEG events reliably

Abstract

This paper deals with the detection of mu-suppression from electroencephalographic (EEG) signals in brain-computer interface (BCI). For this purpose, an efficient algorithm is proposed based on a statistical model and a linear classifier. Precisely, the generalized extreme value distribution (GEV) is proposed to represent the power spectrum density of the EEG signal in the central motor cortex. The associated three parameters are estimated using the maximum likelihood method. Based on these parameters, a simple and efficient linear classifier was designed to classify three types of events: imagery, movement, and resting. Preliminary results show that the proposed statistical model can be used in order to detect precisely the mu-suppression and distinguish different EEG events, with very good classification accuracy.