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Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.

Lichao Xu1, Minpeng Xu1,2, Tzyy-Ping Jung1,2,3

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.

Cognitive Neurodynamics
|August 9, 2021
PubMed
Summary
This summary is machine-generated.

This review covers recent advancements in brain-computer interfaces (BCIs), focusing on electroencephalography (EEG) based systems. It details new encoding paradigms and decoding algorithms essential for BCI development.

Keywords:
BCIDecoding algorithmsEEGEncoding paradigmsReview

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Brain-computer interfaces (BCIs) offer direct human-machine communication.
  • Recent years have seen significant innovation in BCI paradigms and algorithms.
  • A comprehensive review is needed to capture the latest progress in BCIs.

Purpose of the Study:

  • To summarize recent progress in electroencephalography (EEG)-based BCIs.
  • To provide an overview of modern BCI encoding paradigms and decoding algorithms.
  • To aid researchers and developers in the BCI field.

Main Methods:

  • Categorization of encoding paradigms by neural mechanisms: sensory/motor-related, vision-related, cognition-related, and hybrid.
  • Review of decoding algorithms into four categories: decomposition, Riemannian geometry, deep learning, and transfer learning.

Main Results:

  • EEG-based BCIs have advanced through novel encoding strategies and sophisticated decoding techniques.
  • The review systematically categorizes these advancements for clarity.
  • Key elements of BCI systems (encoding and decoding) are thoroughly examined.

Conclusions:

  • This paper offers a comprehensive overview of current EEG-based BCI paradigms and algorithms.
  • Understanding these advancements is crucial for future BCI system development.
  • The review serves as a valuable resource for the BCI research community.