Interpretation of neural signals to a form that is as intelligible as speech facilitates the development of communication mediums for the otherwise speech/motor-impaired individuals. Speech perception, production, and imagination often constitute phases of human communication. The primary goal of our work is to analyze the similarity between these three phases by studying electroencephalogram(EEG) patterns across these modalities, in order to establish their usefulness for brain-computer interfaces. Neural decoding of speech using such non-invasive techniques necessitates the optimal choice of signal analysis and translation protocols. By employing selection-by-exclusion based temporal modeling algorithms, we discover fundamental syllable-like units that reveal a similar set of signal signatures across all the three phases. Significantly higher than chance accuracies are recorded for single-trial multi-unit EEG classification using machine learning approaches over three datasets across 30 subjects. Repeatability and subject independence tests performed at every step of the analysis further strengthens the findings and holds promise for translating brain signals to speech non-invasively.

Further, we explore the concept of "brain silence", the response of the brain to silence portions in speech. Analogous to how voice activity detection is employed to enhance the performance of speech recognition, an EEG state activity detection protocol is applied to boost the confidence of imagined speech EEG decoding. The recognition performance and the visual distinction observed demonstrates the existence of distinct silence signatures in EEG.