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An asynchronous Brain--Computer Interface (BCI) based on imagined speech is atool that allows to control an external device or to emit a message at themoment the user desires to by decoding EEG signals of imagined speech. In orderto correctly implement these types of BCI, we must be able to detect from acontinuous signal, when the subject starts to imagine words. In this work, fivemethods of feature extraction based on wavelet decomposition, empirical modedecomposition, frequency energies, fractal dimension and chaos theory featuresare presented to solve the task of detecting imagined words segments fromcontinuous EEG signals as a preliminary study for a latter implementation of anasynchronous BCI based on imagined speech. These methods are tested in threedatasets using four different classifiers and the higher F1 scores obtained are0.73, 0.79, and 0.68 for each dataset, respectively. This results are promisingto build a system that automatizes the segmentation of imagined words segmentsfor latter classification.

Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals