Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine

Kang Wang; Di Hua Zhai; Yuanqing Xia

doi:10.23919/ChiCC.2019.8866574

Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine

Kang Wang, Di Hua Zhai^*, Yuanqing Xia

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Establishing an accurate and rapid electroencephalography (EEG) recognition algorithm is an important research direction in the field of Brain-Computer Interface (BCI). In this paper, EEG recognition algorithm is constructed based on discrete wavelet transform (DWT), common spatial patterns (CSP) and extreme learning machine (ELM). DWT and CSP are used for joint feature extraction, which solves the problem that traditional CSP is sensitive to noise. ELM is used for classification, which improves the real-time performance of the BCI system. Our findings show a classification accuracy of 90% and a classification time of 0.012s for the Data Set III in BCI Competition 2003, which proves the effectiveness of the algorithm.

Original language	English
Title of host publication	Proceedings of the 38th Chinese Control Conference, CCC 2019
Editors	Minyue Fu, Jian Sun
Publisher	IEEE Computer Society
Pages	4590-4595
Number of pages	6
ISBN (Electronic)	9789881563972
DOIs	https://doi.org/10.23919/ChiCC.2019.8866574
Publication status	Published - Jul 2019
Event	38th Chinese Control Conference, CCC 2019 - Guangzhou, China Duration: 27 Jul 2019 → 30 Jul 2019

Publication series

Name	Chinese Control Conference, CCC
Volume	2019-July
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	38th Chinese Control Conference, CCC 2019
Country/Territory	China
City	Guangzhou
Period	27/07/19 → 30/07/19

Keywords

Common spatial patterns
Discrete wavelet transform
Electroencephalography
Extreme learning machine

Access to Document

10.23919/ChiCC.2019.8866574

Cite this

Wang, K., Zhai, D. H., & Xia, Y. (2019). Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine. In M. Fu, & J. Sun (Eds.), Proceedings of the 38th Chinese Control Conference, CCC 2019 (pp. 4590-4595). Article 8866574 (Chinese Control Conference, CCC; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2019.8866574

@inproceedings{f2d36bf72d0c461998c59a351b14b477,

title = "Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine",

abstract = "Establishing an accurate and rapid electroencephalography (EEG) recognition algorithm is an important research direction in the field of Brain-Computer Interface (BCI). In this paper, EEG recognition algorithm is constructed based on discrete wavelet transform (DWT), common spatial patterns (CSP) and extreme learning machine (ELM). DWT and CSP are used for joint feature extraction, which solves the problem that traditional CSP is sensitive to noise. ELM is used for classification, which improves the real-time performance of the BCI system. Our findings show a classification accuracy of 90% and a classification time of 0.012s for the Data Set III in BCI Competition 2003, which proves the effectiveness of the algorithm.",

keywords = "Common spatial patterns, Discrete wavelet transform, Electroencephalography, Extreme learning machine",

author = "Kang Wang and Zhai, {Di Hua} and Yuanqing Xia",

note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",

year = "2019",

month = jul,

doi = "10.23919/ChiCC.2019.8866574",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "4590--4595",

editor = "Minyue Fu and Jian Sun",

booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",

address = "United States",

}

Wang, K, Zhai, DH & Xia, Y 2019, Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine. in M Fu & J Sun (eds), Proceedings of the 38th Chinese Control Conference, CCC 2019., 8866574, Chinese Control Conference, CCC, vol. 2019-July, IEEE Computer Society, pp. 4590-4595, 38th Chinese Control Conference, CCC 2019, Guangzhou, China, 27/07/19. https://doi.org/10.23919/ChiCC.2019.8866574

Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine. / Wang, Kang; Zhai, Di Hua ; Xia, Yuanqing.
Proceedings of the 38th Chinese Control Conference, CCC 2019. ed. / Minyue Fu; Jian Sun. IEEE Computer Society, 2019. p. 4590-4595 8866574 (Chinese Control Conference, CCC; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine

AU - Wang, Kang

AU - Zhai, Di Hua

AU - Xia, Yuanqing

PY - 2019/7

Y1 - 2019/7

N2 - Establishing an accurate and rapid electroencephalography (EEG) recognition algorithm is an important research direction in the field of Brain-Computer Interface (BCI). In this paper, EEG recognition algorithm is constructed based on discrete wavelet transform (DWT), common spatial patterns (CSP) and extreme learning machine (ELM). DWT and CSP are used for joint feature extraction, which solves the problem that traditional CSP is sensitive to noise. ELM is used for classification, which improves the real-time performance of the BCI system. Our findings show a classification accuracy of 90% and a classification time of 0.012s for the Data Set III in BCI Competition 2003, which proves the effectiveness of the algorithm.

AB - Establishing an accurate and rapid electroencephalography (EEG) recognition algorithm is an important research direction in the field of Brain-Computer Interface (BCI). In this paper, EEG recognition algorithm is constructed based on discrete wavelet transform (DWT), common spatial patterns (CSP) and extreme learning machine (ELM). DWT and CSP are used for joint feature extraction, which solves the problem that traditional CSP is sensitive to noise. ELM is used for classification, which improves the real-time performance of the BCI system. Our findings show a classification accuracy of 90% and a classification time of 0.012s for the Data Set III in BCI Competition 2003, which proves the effectiveness of the algorithm.

KW - Common spatial patterns

KW - Discrete wavelet transform

KW - Electroencephalography

KW - Extreme learning machine

UR - http://www.scopus.com/inward/record.url?scp=85074440564&partnerID=8YFLogxK

U2 - 10.23919/ChiCC.2019.8866574

DO - 10.23919/ChiCC.2019.8866574

M3 - Conference contribution

AN - SCOPUS:85074440564

T3 - Chinese Control Conference, CCC

SP - 4590

EP - 4595

BT - Proceedings of the 38th Chinese Control Conference, CCC 2019

A2 - Fu, Minyue

A2 - Sun, Jian

PB - IEEE Computer Society

T2 - 38th Chinese Control Conference, CCC 2019

Y2 - 27 July 2019 through 30 July 2019

ER -

Motor imagination EEG recognition algorithm based on DWT, CSP and extreme learning machine

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this