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Authors: Sylvie Charbonnier 1 ; Raphaelle Roy 1 ; Radka Doležalová 1 ; Aurélie Campagne 1 and Stéphane Bonnet 2

Affiliations: 1 Univ. Grenoble Alpes and CNRS, France ; 2 Univ. Grenoble Alpes, France

Keyword(s): Workload, EEG, Connectivity.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Working memory load can be estimated using features extracted from the electroencephalogram (EEG). Connectivity measures, that evaluate the interaction between signals, can be used to extract such features and therefore provide information about the interconnection of brain areas and electrode sites. To our knowledge, there is no literature regarding a direct comparison of the relevance of several connectivity measures for working memory load estimation. This study intends to overcome this lack of literature by proposing a direct comparison of four connectivity measures on data extracted from a working memory load experiment performed by 20 participants. These features are extracted using pattern-based or vector-based methods, and classified using an FLDA classifier and a 10-fold cross-validation procedure. The relevance of the connectivity measures was assessed by statistically comparing the obtained classification accuracy. Additional investigations were performed regarding the bes t set of electrodes and the best frequency band. The main results are that covariance seems to be the best connectivity measure to estimate working memory load from EEG signals, even more so with signals filtered in the beta band. point. (More)

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Paper citation in several formats:
Charbonnier, S.; Roy, R.; Doležalová, R.; Campagne, A. and Bonnet, S. (2016). Estimation of Working Memory Load using EEG Connectivity Measures. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0; ISSN 2184-4305, pages 122-128. DOI: 10.5220/0005638201220128

@conference{biosignals16,
author={Sylvie Charbonnier. and Raphaelle Roy. and Radka Doležalová. and Aurélie Campagne. and Stéphane Bonnet.},
title={Estimation of Working Memory Load using EEG Connectivity Measures},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={122-128},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005638201220128},
isbn={978-989-758-170-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, (BIOSTEC 2016)
TI - Estimation of Working Memory Load using EEG Connectivity Measures
SN - 978-989-758-170-0
IS - 2184-4305
AU - Charbonnier, S.
AU - Roy, R.
AU - Doležalová, R.
AU - Campagne, A.
AU - Bonnet, S.
PY - 2016
SP - 122
EP - 128
DO - 10.5220/0005638201220128