Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction

Ji Hoon Jeong, Jeong Hyun Cho, Byeong Hoo Lee, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

Original languageEnglish
Pages (from-to)7469-7482
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number12
Publication statusPublished - 2023 Dec 1

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Brain - computer interface
  • deep neurolinguistic learning
  • electroencephalogram (EEG)
  • neural language ecoding

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


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