Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
Author:
Affiliation:
1. Korea University,Department of Brain and Cognitive Engineering,Seoul,Republic of Korea,02841
2. Korea University,Department of Artificial Intelligence,Seoul,Republic of Korea,02841
Funder
‘Defense Challengeable Future Technology Program’ of Agency for Defense Development
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871721.pdf?arnumber=9871721
Reference24 articles.
1. Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts
2. Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks
3. Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features
4. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy
5. Imagined speech can be decoded from low- and cross-frequency intracranial EEG features
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1. End-to-end translation of human neural activity to speech with a dual–dual generative adversarial network;Knowledge-Based Systems;2023-10
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