  A Brain Computer Interface based on imagined words can decode the word a
subject is thinking on through brain signals to control an external device. In
order to build a fully asynchronous Brain Computer Interface based on imagined
words in electroencephalogram signals as source, we need to solve the problem
of detecting the onset of the imagined words. Although there has been some
research in this field, the problem has not been fully solved. In this paper we
present an approach to solve this problem by using values from statistics,
information theory and chaos theory as features to correctly identify the onset
of imagined words in a continuous signal. On detecting the onsets of imagined
words, the highest True Positive Rate achieved by our approach was obtained
using features based on the generalized Hurst exponent, this True Positive Rate
was 0.69 and 0.77 with a timing error tolerance region of 3 and 4 seconds
respectively.
