  Every people has their own voice, likewise, brain signals dis-play distinct
neural representations for each individual. Al-though recent studies have
revealed the robustness of speech-related paradigms for efficient
brain-computer interface, the dis-tinction on their cognitive representations
with practical usabil-ity still remains to be discovered. Herein, we
investigate the dis-tinct brain patterns from electroencephalography (EEG)
duringimagined speech, overt speech, and speech perception in termsof subject
variations with its practical use of speaker identifica-tion from single
channel EEG. We performed classification ofnine subjects using deep neural
network that captures temporal-spectral-spatial features from EEG of imagined
speech, overtspeech, and speech perception. Furthermore, we demonstratedthe
underlying neural features of individual subjects while per-forming imagined
speech by comparing the functional connec-tivity and the EEG envelope features.
Our results demonstratethe possibility of subject identification from single
channel EEGof imagined speech and overt speech. Also, the comparison ofthe
three speech-related paradigms will provide valuable infor-mation for the
practical use of speech-related brain signals inthe further studies.
