Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition.

Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon.

Extreme Learning Machine Design for Dealing with Unrepresentative Features.

Extreme Learning Machines (ELMs) have become a popular tool for the classification of electroencephalography (EEG) signals for Brain Computer Interfaces. This is so mainly due to their very high training speed and generalization capabilities. Another important advantage is that they have only one hyperparameter that must be calibrated: the number of hidden nodes.

Dynamic speech parameterization for text-independent phone segmentation.

In this work, a dynamic speech parameterization based on the continuous multiresolution divergence is used to modify a text-independent phone segmentation algorithm. This encoding is employed as input and also replaces an stage of the segmentation procedure responsible for the estimation of the intensity of changes in signal features. The segmentation performance of this representation has been compared with the original algorithm using as input a classical Melbank parameterization and speech representation based on the continuous multiresolution divergence.