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


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)1-14
Number of pages14
JournalIEEE Transactions on Cybernetics
Publication statusAccepted/In press - 2022


  • Brain modeling
  • Brain–computer interface
  • Decoding
  • deep neurolinguistic learning
  • electroencephalogram (EEG)
  • Electroencephalography
  • neural language decoding
  • Prosthetics
  • Real-time systems
  • Spectrogram
  • Task analysis

ASJC Scopus subject areas

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


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