  Speech cognition bears potential application as a brain computer interface
that can improve the quality of life for the otherwise communication impaired
people. While speech and resting state EEG are popularly studied, here we
attempt to explore a "non-speech"(NS) state of brain activity corresponding to
the silence regions of speech audio. Firstly, speech perception is studied to
inspect the existence of such a state, followed by its identification in speech
imagination. Analogous to how voice activity detection is employed to enhance
the performance of speech recognition, the EEG state activity detection
protocol implemented here is applied to boost the confidence of imagined speech
EEG decoding. Classification of speech and NS state is done using two datasets
collected from laboratory-based and commercial-based devices. The state
sequential information thus obtained is further utilized to reduce the search
space of imagined EEG unit recognition. Temporal signal structures and
topographic maps of NS states are visualized across subjects and sessions. The
recognition performance and the visual distinction observed demonstrates the
existence of silence signatures in EEG.
