2021/05/31 by Seo‐Hyun Lee, Lee, Seo-Hyun, Young Eun Lee +3
Computer Science · Neuroscience · #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.2105.14787
openalex publication_date 2021/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.