  An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a
tool that allows to control an external device or to emit a message at the
moment the user desires to by decoding EEG signals of imagined speech. In order
to correctly implement these types of BCI, we must be able to detect from a
continuous signal, when the subject starts to imagine words. In this work, five
methods of feature extraction based on wavelet decomposition, empirical mode
decomposition, frequency energies, fractal dimension and chaos theory features
are presented to solve the task of detecting imagined words segments from
continuous EEG signals as a preliminary study for a latter implementation of an
asynchronous BCI based on imagined speech. These methods are tested in three
datasets using four different classifiers and the higher F1 scores obtained are
0.73, 0.79, and 0.68 for each dataset, respectively. This results are promising
to build a system that automatizes the segmentation of imagined words segments
for latter classification.
