Detection of mental fatigue using an active BCI inspired signal processing chain

Abstract

This paper presents a novel electroencephalography (EEG) signal processing chain designed to classify two levels of mental fatigue that appears after having spent a long time on a tedious task. The decrease in vigilance associated with mental fatigue makes it a dangerous state for operators in charge of complex systems. The processing chain, inspired from active brain computer interface computing, is implemented as follows: the EEG signal is initially filtered in a given frequency band and 15 electrodes out of 32 are then selected using a method based on Riemannian geometry. Next, a spatial filtering step is carried out using 6 common spatial pattern (CSP) filters. Lastly, a binary classification is performed using Fisher’s linear discriminant analysis (FLDA). The features used are the log variance of the 6 CSP filtered signals. The results obtained on 20 healthy volunteers are excellent with 100% of accuracy when the beta band is used. These performances drop to 84% and 68% when the same data are processed with a traditional signal processing chain where fatigue is classified by means of a FLDA classifier fed by the averaged power, or relative power, in the beta band extracted from 15 selected electrodes.