Comparison of the ICA and PCA Methods in Correction of EEG Signal Artefacts

被引：0

作者：

Kaczorowska, Monika ^{[1
]}

Plechawska-Wojcik, Malgorzata ^{[1
]}

Tokovarov, Mikhail ^{[1
]}

Dmytruk, Roman ^{[1
]}

机构：

[1] Lublin Univ Technol, Fac Elect Engn & Comp Sci, Inst Comp Sci, Lublin, Poland

来源：

2017 10TH INTERNATIONAL SYMPOSIUM ON ADVANCED TOPICS IN ELECTRICAL ENGINEERING (ATEE) | 2017年

关键词：

EEG signal; Principal Component Analysis; Independent Component Analysis; BLIND SEPARATION; REMOVAL;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper presents application and comparison of two methods based on the blind source separation problem: Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as well as combining these methods. Both methods might be applied in the task of eliminating artefacts from the electroencefalography (EEG) signal. Such artefacts might cover eye-blinks, muscle artefacts etc. The case study described in the paper presents the results of correcting various kinds of artefacts using these methods and its comparison to manual artefact detection performed by an expert.

引用

页码：262 / 267

页数：6

共 50 条

[31] Extracting structural damage features: Comparison between PCA and ICA
Zhong, Luo
Song, Huazhu
Han, Bo
INTELLIGENT COMPUTING IN SIGNAL PROCESSING AND PATTERN RECOGNITION, 2006, 345 : 840 - 845
[32] EEG SIGNAL WITH FEATURE EXTRACTION USING SVM AND ICA CLASSIFIERS
Kumar, P. Nirmal
Kareemullah, H.
2014 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES), 2014,
[33] A comparison of PCA and ICA for object recognition under varying illumination
Fortuna, J
Schuurman, D
Capson, D
16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL III, PROCEEDINGS, 2002, : 11 - 15
[34] VOG-ENHANCED ICA FOR REMOVING BLINK AND EYE-MOVEMENT ARTEFACTS FROM EEG
Samadi, Mohammad Reza Haji
Zakeri, Zohreh
Cooke, Neil
2016 3RD IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS, 2016, : 603 - 606
[35] Comparison of PCA and music on EEG source localization
Mühlnickel, W
Lutzenberger, W
Flor, H
EUROPEAN JOURNAL OF NEUROSCIENCE, 1998, 10 : 135 - 135
[36] Comparison of linear, nonlinear, and feature selection methods for EEG signal classification
Garrett, D
Peterson, DA
Anderson, CW
Thaut, MH
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2003, 11 (02) : 141 - 144
[37] Comparison of linear, nonlinear and feature selection methods for EEG signal classification
Rodionov, AS
L'vov, AA
APEDE 2004: INTERNATIONAL CONFERENCE ON ACTUAL PROBLEMS OF ELECTRON DEVICES ENGINEERING, CONFERENCE PROCEEDINGS, 2004, : 436 - 439
[38] A fair comparison of the EEG signal classification methods for alcoholic subject identification
Awrangjeb, Mohammad
Rodrigues, Jardel das C.
Stantic, Bela
Estivill-Castro, Vladimir
2020 35TH INTERNATIONAL CONFERENCE ON IMAGE AND VISION COMPUTING NEW ZEALAND (IVCNZ), 2020,
[39] A comparison of ICA algorithms in biomedical signal processing
Azzerboni, B.
Ipsale, M.
La Foresta, F.
Mammone, N.
Morabito, F. C.
BIOLOGICAL AND ARTIFICIAL INTELLIGENCE ENVIRONMENTS, 2005, : 313 - 320
[40] A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine
Cao, LJ
Chua, KS
Chong, WK
Lee, HP
Gu, QM
NEUROCOMPUTING, 2003, 55 (1-2) : 321 - 336

← 1 2 3 4 5 →