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Experiences and insights from the collection of a novel multimedia EEG dataset

Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Wang, Zhengwei orcid logoORCID: 0000-0001-7706-553X, Ward, Tomás E. orcid logoORCID: 0000-0002-6173-6607, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2019) Experiences and insights from the collection of a novel multimedia EEG dataset. In: 26th International Conference on Multimedia Modeling (MMM2020), 5-8 Jan 2020, Daejeon, Korea (Republic of). ISBN 978-3-030-37733-5

Abstract
There is a growing interest in utilising novel signal sources such as EEG (Electroencephalography) in multimedia research. When using such signals, subtle limitations are often not readily apparent without significant domain expertise. Multimedia research outputs incorporating EEG signals can fail to be replicated when only minor modifications have been made to an experiment or seemingly unimportant (or unstated) details are changed. This can lead to overoptimistic or overpessimistic viewpoints on the potential real-world utility of these signals in multimedia research activities. This paper describes an EEG/MM dataset and presents a summary of distilled experiences and knowledge gained during the preparation (and utilisiation) of the dataset that supported a collaborative neural-image labelling benchmarking task. The goal of this task was to collaboratively identify machine learning approaches that would support the use of EEG signals in areas such as image labelling and multimedia modeling or retrieval. The contributions of this paper can be listed thus; a template experimental paradigm is proposed (along with datasets and a baseline system) upon which researchers can explore multimedia image labelling using a brain-computer interface, learnings regarding commonly encountered issues (and useful signals) when conducting research that utilises EEG in multimedia contexts are provided, and finally insights are shared on how an EEG dataset was used to support a collaborative neural-image labelling benchmarking task and the valuable experiences gained.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Brain-computer Interface; Electroencephalography; RSVP
Subjects:Biological Sciences > Neuroscience
Computer Science > Information retrieval
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 26th International Conference, MMM 2020, Proceedings, Part II. Lecture Notes in Computer Science 11962. Springer, Cham. ISBN 978-3-030-37733-5
Publisher:Springer, Cham
Official URL:http://dx.doi.org/10.1007/978-3-030-37734-2_39
Copyright Information:© 2020 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Insight Centre for Data Analytics (which is supported by Science Foundation Ireland under Grant Number SFI/12/RC/2289), Dublin City University’s Research Committee
ID Code:24649
Deposited On:18 Jun 2020 15:44 by Graham Healy . Last Modified 18 Jun 2020 15:44
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