VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG. / Haji Samadi, Mohammad; Cooke, Neil.

Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European. IEEE Press / Wiley, 2014. p. 2025-2029 6952745.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Haji Samadi, M & Cooke, N 2014, VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG. in Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European., 6952745, IEEE Press / Wiley, pp. 2025-2029, 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, United Kingdom, 1/09/14. <http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6952745>

APA

Haji Samadi, M., & Cooke, N. (2014). VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG. In Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European (pp. 2025-2029). [6952745] IEEE Press / Wiley. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6952745

Vancouver

Haji Samadi M, Cooke N. VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG. In Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European. IEEE Press / Wiley. 2014. p. 2025-2029. 6952745

Author

Haji Samadi, Mohammad ; Cooke, Neil. / VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG. Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European. IEEE Press / Wiley, 2014. pp. 2025-2029

Bibtex

@inproceedings{2b66da1f8494430ab2dd9306af481819,
title = "VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG",
abstract = "The steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) paradigm detects when users look at flashing static and dynamic visual stimuli. Electroculogram (EOG) artefacts in the electroencephalography (EEG) signal limit the application for dynamic stimuli because they elicit smooth pursuit eye movement. We propose VOG-ICA - an EOG artefact rejection technique based on Independent Component Analysis (ICA) that uses video-oculography (VOG) information from an eye tracker. It demonstrates good performance compared to Pl{\"o}chl when evaluated on matched and EEG data collected with consumer grade eye tracking and wireless cap EEG apparatus. SSVEP response detection from frequential features extracted from ICA components demonstrates higher SSVEP response detection accuracy and lower between-person variation compared with extracted features from raw and post-ICA reconstructed clean EEG. The work highlights the requirement for robust EEG artefact and SSVEP response detection techniques for consumer-grade multimodal apparatus.",
keywords = "Artefact Rejection, EEG, ICA, SSVEP, VOG",
author = "{Haji Samadi}, Mohammad and Neil Cooke",
year = "2014",
language = "English",
isbn = "9780992862619",
pages = "2025--2029",
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 - VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG

AU - Haji Samadi, Mohammad

AU - Cooke, Neil

PY - 2014

Y1 - 2014

N2 - The steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) paradigm detects when users look at flashing static and dynamic visual stimuli. Electroculogram (EOG) artefacts in the electroencephalography (EEG) signal limit the application for dynamic stimuli because they elicit smooth pursuit eye movement. We propose VOG-ICA - an EOG artefact rejection technique based on Independent Component Analysis (ICA) that uses video-oculography (VOG) information from an eye tracker. It demonstrates good performance compared to Plöchl when evaluated on matched and EEG data collected with consumer grade eye tracking and wireless cap EEG apparatus. SSVEP response detection from frequential features extracted from ICA components demonstrates higher SSVEP response detection accuracy and lower between-person variation compared with extracted features from raw and post-ICA reconstructed clean EEG. The work highlights the requirement for robust EEG artefact and SSVEP response detection techniques for consumer-grade multimodal apparatus.

AB - The steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) paradigm detects when users look at flashing static and dynamic visual stimuli. Electroculogram (EOG) artefacts in the electroencephalography (EEG) signal limit the application for dynamic stimuli because they elicit smooth pursuit eye movement. We propose VOG-ICA - an EOG artefact rejection technique based on Independent Component Analysis (ICA) that uses video-oculography (VOG) information from an eye tracker. It demonstrates good performance compared to Plöchl when evaluated on matched and EEG data collected with consumer grade eye tracking and wireless cap EEG apparatus. SSVEP response detection from frequential features extracted from ICA components demonstrates higher SSVEP response detection accuracy and lower between-person variation compared with extracted features from raw and post-ICA reconstructed clean EEG. The work highlights the requirement for robust EEG artefact and SSVEP response detection techniques for consumer-grade multimodal apparatus.

KW - Artefact Rejection

KW - EEG

KW - ICA

KW - SSVEP

KW - VOG

UR - http://www.scopus.com/inward/record.url?scp=84911941539&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9780992862619

SP - 2025

EP - 2029

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 -