Mental fatigue is one of the main causes of human performance failures, leading to accidents in vehicle operation, air traffic control and space missions. Therefore, automatic detection of early signs of mental fatigue is key for increasing safety and human performance in many scenarios.
Electroencephalograms (EEGs) are considered the most informative signals for monitoring mental fatigue among several other physiological and behavioral measures available. We study multiple EEG signals recorded in subjects who performed continuous mental arithmetic for an extended period of time. Specifically, we analyze these EEG signals using a multi-process approach in which each of the processes representing a particular mental state is modelled with an autoregression (AR). We impose structured prior distributions on the model parameters. Such priors take into account the latent components underlying each autoregressive process, allowing us to incorporate relevant information about the components that characterize different states of alertness. We discuss issues related to on-line filtering and automatic detection of fatigue from multi-channel data.
