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

Lichao Xu; Minpeng Xu; Tzyy-Ping Jung; Dong Ming

doi:10.1007/s11571-021-09676-z

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

Cogn Neurodyn. 2021 Aug;15(4):569-584. doi: 10.1007/s11571-021-09676-z. Epub 2021 Apr 10.

Authors

Lichao Xu¹, Minpeng Xu^{1

2}, Tzyy-Ping Jung^{1

2

3}, Dong Ming^{1

2}

Affiliations

¹ Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
² Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
³ Swartz Center for Computational Neuroscience, University of California, San Diego, USA.

Abstract

A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.

Keywords: BCI; Decoding algorithms; EEG; Encoding paradigms; Review.

Publication types

Review