Robust maximum signal fraction analysis for blind source separation-Reference-Cited by-同舟云学术

Robust maximum signal fraction analysis for blind source separation

Published:2017-10 Issue:8 Volume:11 Page:969-974
ISSN:1751-9675
Container-title:IET Signal Processing
language:en
Short-container-title:IET signal process.

Author:

Lu Xuesong¹,Li Xiaomeng²,Fu Mao‐sheng³,Wang Haixian⁴

Affiliation:

1. Department of Rehabilitation, Zhongda HospitalSoutheast UniversityNanjing210009JiangsuPeople's Republic of China

2. Research Center for Learning ScienceSoutheast UniversityNanjing210096JiangsuPeople's Republic of China

3. School of Electronics and Information EngineeringWest Anhui UniversityLu'an237012AnhuiPeople's Republic of China

4. School of Mathematics and Big DataFoshan UniversityFoshan528000GuangdongPeople's Republic of China

Funder

Natural Science Foundation of Guangdong Province

Science and Technology Planning Project of Guangdong Province

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-spr.2016.0529

Reference20 articles.

1. From Blind Signal Extraction to Blind Instantaneous Signal Separation: Criteria, Algorithms, and Stability

2. Spatial frequency channels derived from individual differences

3. Cichocki A. Vorobyov S.: ‘Application of ICA for automatic noise and interference cancellation in multisensory biomedical signals’.Proc. 2nd Int. Workshop ICA BSS June2000 pp.621–626

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Maximum Signal Fraction Analysis for Enhancing Signal-to-Noise Ratio of EEG Signals in SSVEP-Based BCIs;IEEE Access;2019

2. A Computationally Efficient Blind Source Extraction Using Idempotent Transformation Matrix;Circuits, Systems, and Signal Processing;2018-10-12

3. Efficient blind source extraction of noisy mixture utilising a class of parallel linear predictor filters;IET Signal Processing;2018-10