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

doi:10.1007/s11571-021-09676-z

Review

. 2021 Aug;15(4):569-584.

doi: 10.1007/s11571-021-09676-z. Epub 2021 Apr 10.

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

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.

PMID: 34367361
PMCID: PMC8286913
DOI: 10.1007/s11571-021-09676-z

Review

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

Lichao Xu et al. Cogn Neurodyn. 2021 Aug.

. 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.

PMID: 34367361
PMCID: PMC8286913
DOI: 10.1007/s11571-021-09676-z

Erratum in

Correction to: Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.
Xu L, Xu M, Jung TP, Ming D. Xu L, et al. Cogn Neurodyn. 2021 Oct;15(5):921. doi: 10.1007/s11571-021-09686-x. Epub 2021 Jun 5. Cogn Neurodyn. 2021. PMID: 34603552 Free PMC article.

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.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

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References

1. Abdelfattah SM, Abdelrahman GM, Wang M (2018) Augmenting the size of EEG datasets using generative adversarial networks. In: International joint conference on neural networks (IJCNN). IEEE, pp 1–6
1. Acqualagna L, Blankertz B. Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP) Clin Neurophysiol. 2013;124(5):901–908. doi: 10.1016/j.clinph.2012.12.050. - DOI - PubMed
1. Acqualagna L, Treder MS, Schreuder M, Blankertz B (2010) A novel brain–computer interface based on the rapid serial visual presentation paradigm. In: Annual international conference of the IEEE engineering in medicine and biology. IEEE, pp 2686–2689 - PubMed
1. Ahmadi A, Davoudi S, Behroozi M, Daliri MR. Decoding covert visual attention based on phase transfer entropy. Physiol Behav. 2020;222:112932. doi: 10.1016/j.physbeh.2020.112932. - DOI - PubMed
1. Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G. Toward a hybrid brain–computer interface based on imagined movement and visual attention. J Neural Eng. 2010;7(2):026007. doi: 10.1088/1741-2560/7/2/026007. - DOI - PubMed

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[1] Abdelfattah SM, Abdelrahman GM, Wang M (2018) Augmenting the size of EEG datasets using generative adversarial networks. In: International joint conference on neural networks (IJCNN). IEEE, pp 1–6

[2] Abdelfattah SM, Abdelrahman GM, Wang M (2018) Augmenting the size of EEG datasets using generative adversarial networks. In: International joint conference on neural networks (IJCNN). IEEE, pp 1–6

[3] Acqualagna L, Blankertz B. Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP) Clin Neurophysiol. 2013;124(5):901–908. doi: 10.1016/j.clinph.2012.12.050. - DOI - PubMed

[4] Acqualagna L, Blankertz B. Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP) Clin Neurophysiol. 2013;124(5):901–908. doi: 10.1016/j.clinph.2012.12.050. - DOI - PubMed

[5] Acqualagna L, Treder MS, Schreuder M, Blankertz B (2010) A novel brain–computer interface based on the rapid serial visual presentation paradigm. In: Annual international conference of the IEEE engineering in medicine and biology. IEEE, pp 2686–2689 - PubMed

[6] Acqualagna L, Treder MS, Schreuder M, Blankertz B (2010) A novel brain–computer interface based on the rapid serial visual presentation paradigm. In: Annual international conference of the IEEE engineering in medicine and biology. IEEE, pp 2686–2689 - PubMed

[7] Ahmadi A, Davoudi S, Behroozi M, Daliri MR. Decoding covert visual attention based on phase transfer entropy. Physiol Behav. 2020;222:112932. doi: 10.1016/j.physbeh.2020.112932. - DOI - PubMed

[8] Ahmadi A, Davoudi S, Behroozi M, Daliri MR. Decoding covert visual attention based on phase transfer entropy. Physiol Behav. 2020;222:112932. doi: 10.1016/j.physbeh.2020.112932. - DOI - PubMed

[9] Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G. Toward a hybrid brain–computer interface based on imagined movement and visual attention. J Neural Eng. 2010;7(2):026007. doi: 10.1088/1741-2560/7/2/026007. - DOI - PubMed

[10] Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G. Toward a hybrid brain–computer interface based on imagined movement and visual attention. J Neural Eng. 2010;7(2):026007. doi: 10.1088/1741-2560/7/2/026007. - DOI - PubMed

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

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

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Abstract

Conflict of interest statement

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