Task complexity modulates pilot electroencephalographic activity during real flights.

Most research connecting task performance and neural activity to date has been conducted in laboratory conditions. Thus, field studies remain scarce, especially in extreme conditions such as during real flights. Here, we investigated the effects of flight procedures of varied complexity on the in-flight EEG activity of military helicopter pilots.

Efficient automatic selection and combination of EEG features in least squares classifiers for motor imagery brain-computer interfaces.

Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain-computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with low computational complexity. Features are computed in different time segments using three widely used methods for motor imagery tasks and, then, they are concatenated or averaged in order to take into account the time course variability of the EEG signals.