@inproceedings{0eb14fb0f40a4116af5308f571dae396,
title = "Representation of Articulatory Features in EEG During Speech Production Tasks",
abstract = "Investigating the representation of various speech features in EEG signals is crucial for advancing non-invasive speech decoding. In this study, we compared the EEG representation of two commonly used speech features in speech decoding: articulatory and acoustic features. To achieve this, we collected EEG data from 8 Mandarin-speaking participants while they performed both overt speech and imagined speech tasks. Linear encoding and decoding models were constructed to bridge the connection between EEG signals and speech features. The decoding models for predicting articulatory features demonstrated a better representation of articulatory features in EEG compared to traditional acoustic features. Additionally, scalp topographies of entrainment strength obtained by encoding models revealed strong representation of articulatory features in electrodes of the parietal motor area. These findings will contribute to the further application of articulatory features in EEG-based neural speech decoding.",
keywords = "articulatory feature, EEG, speech production",
author = "Sinan Sun and Longxiang Zhang and Bo Wang and Xihong Wu and Jing Chen",
note = "Publisher Copyright: {\textcopyright}2024 IEEE.; 14th International Symposium on Chinese Spoken Language Processing, ISCSLP 2024 ; Conference date: 07-11-2024 Through 10-11-2024",
year = "2024",
doi = "10.1109/ISCSLP63861.2024.10800095",
language = "English",
series = "2024 14th International Symposium on Chinese Spoken Language Processing, ISCSLP 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "219--223",
editor = "Yanmin Qian and Qin Jin and Zhijian Ou and Zhenhua Ling and Zhiyong Wu and Ya Li and Lei Xie and Jianhua Tao",
booktitle = "2024 14th International Symposium on Chinese Spoken Language Processing, ISCSLP 2024",
address = "United States",
}