  This work focuses on inner speech recognition starting from EEG signals.
Inner speech recognition is defined as the internalized process in which the
person thinks in pure meanings, generally associated with an auditory imagery
of own inner "voice". The decoding of the EEG into text should be understood as
the classification of a limited number of words (commands) or the presence of
phonemes (units of sound that make up words). Speech-related BCIs provide
effective vocal communication strategies for controlling devices through speech
commands interpreted from brain signals, improving the quality of life of
people who have lost the capability to speak, by restoring communication with
their environment. Two public inner speech datasets are analysed. Using this
data, some classification models are studied and implemented starting from
basic methods such as Support Vector Machines, to ensemble methods such as the
eXtreme Gradient Boosting classifier up to the use of neural networks such as
Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory
(BiLSTM). With the LSTM and BiLSTM models, generally not used in the literature
of inner speech recognition, results in line with or superior to those present
in the stateof-the-art are obtained.
