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

Inner speech recognition through electroencephalographic signals