@InProceedings{6952746,
  author = {M. R. {Haji Samadi} and N. Cooke},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {EEG signal processing for eye tracking},
  year = {2014},
  pages = {2030-2034},
  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 = {biomechanics;biomedical electrodes;electroencephalography;electro-oculography;feature extraction;independent component analysis;medical signal processing;neurophysiology;signal classification;visual evoked potentials;EEG signal processing;head-mounted video-oculography eye tracking;camera;peripheral view;electrooculography;EOG eye tracking;electroencephalography;EEG eye tracking;wireless cap scalp-based electrodes;classification algorithms;eye movement detection;foveal attention;steady state visually evoked potential;brain SSVEP response;feature extraction;independent component analysis;band-pass preprocessed EEG channels;nonfacially intrusive EEG brain sensing;Electroencephalography;Feature extraction;Tracking;Visualization;Electrooculography;Electrodes;Accuracy;ICA;SSVEP;VOG;eye tracking;visual attention},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926535.pdf},
}

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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. 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R. {Haji Samadi} and N. Cooke},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {EEG signal processing for eye tracking},\n year = {2014},\n pages = {2030-2034},\n 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. 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The work envisages eye tracking technologies that utilise non-facially intrusive EEG brain sensing via wireless dry contact scalp based electrodes.},\n keywords = {biomechanics;biomedical electrodes;electroencephalography;electro-oculography;feature extraction;independent component analysis;medical signal processing;neurophysiology;signal classification;visual evoked potentials;EEG signal processing;head-mounted video-oculography eye tracking;camera;peripheral view;electrooculography;EOG eye tracking;electroencephalography;EEG eye tracking;wireless cap scalp-based electrodes;classification algorithms;eye movement detection;foveal attention;steady state visually evoked potential;brain SSVEP response;feature extraction;independent component analysis;band-pass preprocessed EEG channels;nonfacially intrusive EEG brain sensing;Electroencephalography;Feature extraction;Tracking;Visualization;Electrooculography;Electrodes;Accuracy;ICA;SSVEP;VOG;eye tracking;visual attention},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926535.pdf},\n}\n\n","author_short":["Haji Samadi, M. 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