Invasive Brain Machine Interface System

Yile Jin; Junjun Chen; Shaomin Zhang; Weidong Chen; Xiaoxiang Zheng

doi:10.1007/978-981-13-2050-7_3

Invasive Brain Machine Interface System

Adv Exp Med Biol. 2019:1101:67-89. doi: 10.1007/978-981-13-2050-7_3.

Authors

Yile Jin^{1

2}, Junjun Chen^{1

2}, Shaomin Zhang^{3

4}, Weidong Chen^{1

5}, Xiaoxiang Zheng^{1

2}

Affiliations

¹ Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China.
² Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
³ Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China. shaomin@zju.edu.cn.
⁴ Department of Biomedical Engineering, Zhejiang University, Hangzhou, China. shaomin@zju.edu.cn.
⁵ College of Computer Science, Zhejiang University, Hangzhou, China.

PMID: 31729672
DOI: 10.1007/978-981-13-2050-7_3

Abstract

Because of high spatial-temporal resolution of neural signals obtained by invasive recording, the invasive brain-machine interfaces (BMI) have achieved great progress in the past two decades. With success in animal research, BMI technology is transferring to clinical trials for helping paralyzed people to restore their lost motor functions. This chapter gives a brief review of BMI development from animal experiments to human clinical studies in the following aspects: (1) BMIs based on rodent animals; (2) BMI based on non-human primates; and (3) pilot BMIs studies in clinical trials. In the end, the chapter concludes with a summary of potential opportunities and future challenges in BMI technology.

Keywords: Brain-machine interfaces; Electrocorticogram; Neural decoding; Neural ensemble recording; Neural stimulation.

Publication types

Review

MeSH terms

Animals
Brain-Computer Interfaces* / standards
Brain-Computer Interfaces* / trends
Clinical Trials as Topic
Humans