Using EEG artifacts for BCI applications

Wanli MA; Dat TRAN; Trung Le; Hong Lin; Shang-Ming Zhou

doi:10.1109/IJCNN.2014.6889496

Using EEG artifacts for BCI applications

Wanli MA, Dat TRAN, Trung Le, Hong Lin, Shang-Ming Zhou

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

11 Citations (Scopus)

Abstract

Brain computer interface (BCI) is about the communication channel between the brain of a human subject and a computerized device. Electroencephalography (EEG) signals are the primary choice as the sources of interpreting the intention of the human subject. EEG signals have a long history of being used in human health for the purposes of studying brain activities and medical diagnosis. EEG signals are very weak and are subject to the contamination from many artifact signals. For the applications in human health, true EEG signals, without the contamination, is highly desirable. However, for the purposes of BCI, where stable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose a BCI, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements.

Original language	English
Title of host publication	2014 International Joint Conference on Neural Networks (IJCNN)
Editors	Derong Liu, Jennie Si
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3628-3635
Number of pages	8
ISBN (Electronic)	9781479914845
ISBN (Print)	9781479914821
DOIs	https://doi.org/10.1109/IJCNN.2014.6889496
Publication status	Published - 6 Jul 2014
Event	2014 International Joint Conference on Neural Networks - Beijing, Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2014 International Joint Conference on Neural Networks
Abbreviated title	IJCNN 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IJCNN.2014.6889496

Cite this

MA, W., TRAN, D., Le, T., Lin, H., & Zhou, S.-M. (2014). Using EEG artifacts for BCI applications. In D. Liu, & J. Si (Eds.), 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 3628-3635). Article 6889496 (Proceedings of the International Joint Conference on Neural Networks). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2014.6889496

@inproceedings{0bd2b0b4e19f4435bce4248013c1a7b6,

title = "Using EEG artifacts for BCI applications",

abstract = "Brain computer interface (BCI) is about the communication channel between the brain of a human subject and a computerized device. Electroencephalography (EEG) signals are the primary choice as the sources of interpreting the intention of the human subject. EEG signals have a long history of being used in human health for the purposes of studying brain activities and medical diagnosis. EEG signals are very weak and are subject to the contamination from many artifact signals. For the applications in human health, true EEG signals, without the contamination, is highly desirable. However, for the purposes of BCI, where stable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose a BCI, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements.",

keywords = "EEG Artifacts, Brain computer interfaces",

author = "Wanli MA and Dat TRAN and Trung Le and Hong Lin and Shang-Ming Zhou",

year = "2014",

month = jul,

day = "6",

doi = "10.1109/IJCNN.2014.6889496",

language = "English",

isbn = "9781479914821",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "3628--3635",

editor = "Derong Liu and Jennie Si",

booktitle = "2014 International Joint Conference on Neural Networks (IJCNN)",

address = "United States",

note = "2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

MA, W, TRAN, D, Le, T, Lin, H & Zhou, S-M 2014, Using EEG artifacts for BCI applications. in D Liu & J Si (eds), 2014 International Joint Conference on Neural Networks (IJCNN)., 6889496, Proceedings of the International Joint Conference on Neural Networks, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 3628-3635, 2014 International Joint Conference on Neural Networks, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889496

Using EEG artifacts for BCI applications. / MA, Wanli; TRAN, Dat; Le, Trung et al.
2014 International Joint Conference on Neural Networks (IJCNN). ed. / Derong Liu; Jennie Si. USA: IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 3628-3635 6889496 (Proceedings of the International Joint Conference on Neural Networks).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Using EEG artifacts for BCI applications

AU - MA, Wanli

AU - TRAN, Dat

AU - Le, Trung

AU - Lin, Hong

AU - Zhou, Shang-Ming

PY - 2014/7/6

Y1 - 2014/7/6

N2 - Brain computer interface (BCI) is about the communication channel between the brain of a human subject and a computerized device. Electroencephalography (EEG) signals are the primary choice as the sources of interpreting the intention of the human subject. EEG signals have a long history of being used in human health for the purposes of studying brain activities and medical diagnosis. EEG signals are very weak and are subject to the contamination from many artifact signals. For the applications in human health, true EEG signals, without the contamination, is highly desirable. However, for the purposes of BCI, where stable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose a BCI, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements.

AB - Brain computer interface (BCI) is about the communication channel between the brain of a human subject and a computerized device. Electroencephalography (EEG) signals are the primary choice as the sources of interpreting the intention of the human subject. EEG signals have a long history of being used in human health for the purposes of studying brain activities and medical diagnosis. EEG signals are very weak and are subject to the contamination from many artifact signals. For the applications in human health, true EEG signals, without the contamination, is highly desirable. However, for the purposes of BCI, where stable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose a BCI, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements.

KW - EEG Artifacts

KW - Brain computer interfaces

UR - http://ieeexplore.ieee.org/document/6889496/

UR - http://www.scopus.com/inward/record.url?scp=84908469358&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2014.6889496

DO - 10.1109/IJCNN.2014.6889496

M3 - Conference contribution

SN - 9781479914821

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 3628

EP - 3635

BT - 2014 International Joint Conference on Neural Networks (IJCNN)

A2 - Liu, Derong

A2 - Si, Jennie

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - 2014 International Joint Conference on Neural Networks

Y2 - 6 July 2014 through 11 July 2014

ER -

Using EEG artifacts for BCI applications

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this