Open AccessRemoval of muscle artefacts from few‐channel EEG recordings based on multivariate empirical mode decomposition and independent vector analysis
Author(s) -
Xu Xueyuan,
Chen Xun,
Zhang Yu
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0191
Subject(s) - electroencephalography , channel (broadcasting) , computer science , hilbert–huang transform , artificial intelligence , pattern recognition (psychology) , multivariate statistics , mode (computer interface) , speech recognition , support vector machine , machine learning , neuroscience , psychology , computer vision , telecommunications , filter (signal processing) , operating system
Electroencephalography (EEG) recordings are often contaminated by muscle artefacts. To address the problem, various methods have been proposed to suppress muscle artefacts from either multichannel or single‐channel EEG recordings. However, there exist few studies for muscle artefact removal from few‐channel EEG recordings. An effective solution for the few‐channel situation by combining multivariate empirical mode decomposition (MEMD) with independent vector analysis (IVA), termed as MEMD‐IVA, is proposed. The proposed method consists of two steps. MEMD is first utilised to decompose a few‐channel EEG recording into intrinsic mode functions (IMFs) and then IVA is applied on the IMFs to separate sources related to muscle activity. The performance of the proposed method on simulated and real‐life data is evaluated. The results demonstrated that MEMD‐IVA outperforms other possible existing methods in a few‐channel situation.