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

Mohammad Haji Samadi, Neil Cooke

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

6 Citations (Scopus)

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 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.

Original languageEnglish
Title of host publicationSignal Processing Conference (EUSIPCO), 2013 Proceedings of the 22nd European
PublisherIEEE Press / Wiley
Pages2025-2029
Number of pages5
ISBN (Print)9780992862619
Publication statusPublished - 2014
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, United Kingdom
Duration: 1 Sep 20145 Sep 2014

Conference

Conference22nd European Signal Processing Conference, EUSIPCO 2014
Country/TerritoryUnited Kingdom
CityLisbon
Period1/09/145/09/14

Keywords

  • Artefact Rejection
  • EEG
  • ICA
  • SSVEP
  • VOG

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG'. Together they form a unique fingerprint.

Cite this