EEG signal processing for eye tracking

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

Standard

EEG signal processing for eye tracking. / Haji Samadi, Mohammad; Cooke, Neil.

Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European. IEEE Press / Wiley, 2014. p. 2030-2034 6952746.

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

Harvard

Haji Samadi, M & Cooke, N 2014, EEG signal processing for eye tracking. in Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European., 6952746, IEEE Press / Wiley, pp. 2030-2034, 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, United Kingdom, 1/09/14. <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6952746>

APA

Haji Samadi, M., & Cooke, N. (2014). EEG signal processing for eye tracking. In Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European (pp. 2030-2034). [6952746] IEEE Press / Wiley. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6952746

Vancouver

Haji Samadi M, Cooke N. EEG signal processing for eye tracking. In Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European. IEEE Press / Wiley. 2014. p. 2030-2034. 6952746

Author

Haji Samadi, Mohammad ; Cooke, Neil. / EEG signal processing for eye tracking. Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European. IEEE Press / Wiley, 2014. pp. 2030-2034

Bibtex

@inproceedings{c961f3593ac04f0084394be4173bccb9,
title = "EEG signal processing for eye tracking",
abstract = "Head-mounted Video-Oculography (VOG) eye tracking is visually intrusive due to a camera in the peripheral view. Electrooculography (EOG) eye tracking is socially intrusive because of face-mounted electrodes. In this work we explore Electroencephalography (EEG) eye tracking from less intrusive wireless cap scalp-based electrodes. Classification algorithms to detect eye movement and the focus of foveal attention are proposed and evaluated on data from a matched dataset of VOG and 16-channel EEG. The algorithms utilise EOG artefacts and the brain's steady state visually evoked potential (SSVEP) response while viewing flickering stimulus. We demonstrate improved performance by extracting features from source signals estimated by Independent Component Analysis (ICA) rather than the traditional band-pass preprocessed EEG channels. The work envisages eye tracking technologies that utilise non-facially intrusive EEG brain sensing via wireless dry contact scalp based electrodes.",
keywords = "eye tracking, ICA, SSVEP, visual attention, VOG",
author = "{Haji Samadi}, Mohammad and Neil Cooke",
year = "2014",
language = "English",
isbn = "9780992862619",
pages = "2030--2034",
booktitle = "Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European",
publisher = "IEEE Press / Wiley",
note = "22nd European Signal Processing Conference, EUSIPCO 2014 ; Conference date: 01-09-2014 Through 05-09-2014",

}

RIS

TY - GEN

T1 - EEG signal processing for eye tracking

AU - Haji Samadi, Mohammad

AU - Cooke, Neil

PY - 2014

Y1 - 2014

N2 - Head-mounted Video-Oculography (VOG) eye tracking is visually intrusive due to a camera in the peripheral view. Electrooculography (EOG) eye tracking is socially intrusive because of face-mounted electrodes. In this work we explore Electroencephalography (EEG) eye tracking from less intrusive wireless cap scalp-based electrodes. Classification algorithms to detect eye movement and the focus of foveal attention are proposed and evaluated on data from a matched dataset of VOG and 16-channel EEG. The algorithms utilise EOG artefacts and the brain's steady state visually evoked potential (SSVEP) response while viewing flickering stimulus. We demonstrate improved performance by extracting features from source signals estimated by Independent Component Analysis (ICA) rather than the traditional band-pass preprocessed EEG channels. The work envisages eye tracking technologies that utilise non-facially intrusive EEG brain sensing via wireless dry contact scalp based electrodes.

AB - Head-mounted Video-Oculography (VOG) eye tracking is visually intrusive due to a camera in the peripheral view. Electrooculography (EOG) eye tracking is socially intrusive because of face-mounted electrodes. In this work we explore Electroencephalography (EEG) eye tracking from less intrusive wireless cap scalp-based electrodes. Classification algorithms to detect eye movement and the focus of foveal attention are proposed and evaluated on data from a matched dataset of VOG and 16-channel EEG. The algorithms utilise EOG artefacts and the brain's steady state visually evoked potential (SSVEP) response while viewing flickering stimulus. We demonstrate improved performance by extracting features from source signals estimated by Independent Component Analysis (ICA) rather than the traditional band-pass preprocessed EEG channels. The work envisages eye tracking technologies that utilise non-facially intrusive EEG brain sensing via wireless dry contact scalp based electrodes.

KW - eye tracking

KW - ICA

KW - SSVEP

KW - visual attention

KW - VOG

UR - http://www.scopus.com/inward/record.url?scp=84911901129&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9780992862619

SP - 2030

EP - 2034

BT - Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European

PB - IEEE Press / Wiley

T2 - 22nd European Signal Processing Conference, EUSIPCO 2014

Y2 - 1 September 2014 through 5 September 2014

ER -