Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Informed perspectives on human annotation using neural signals

Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2016) Informed perspectives on human annotation using neural signals. In: The 22nd International Conference on Multimedia Modeling, 4-6 Jan 2016, Miami, FL.. ISBN 978-3-319-27673-1

Abstract
In this work we explore how neurophysiological correlates related to attention and perception can be used to better understand the image-annotation task. We explore the nature of the highly variable labelling data often seen across annotators. Our results indicate potential issues with regard to ‘how well’ a person manually annotates images and variability across annotators. We propose such issues arise in part as a result of subjectively interpretable instructions that may fail to elicit similar labelling behaviours and decision thresholds across participants. We find instances where an individual’s annotations differ from a group consensus, even though their EEG (Electroencephalography) signals indicate in fact they were likely in consensus with the group. We offer a new perspective on how EEG can be incorporated in an annotation task to reveal information not readily captured using manual annotations alone. As crowd-sourcing resources become more readily available for annotation tasks one can reconsider the quality of such annotations. Furthermore, with the availability of consumer EEG hardware, we speculate that we are approaching a point where it may be feasible to better harness an annotators time and decisions by examining neural responses as part of the process. In this regard, we examine strategies to deal with inter-annotator sources of noise and correlation that can be used to understand the relationship between annotators at a neural level.
Metadata
Item Type:Conference or Workshop Item (Speech)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Brain-computer interface; EEG; HCI; Semantic
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of MMM 2016 - The 22nd International Conference on Multimedia Modeling. Lecture Notes in Computer Science 9517. Springer. ISBN 978-3-319-27673-1
Publisher:Springer
Copyright Information:© 2016 Springer. The original publication is available at www.springerlink.com
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:21017
Deposited On:13 Jan 2016 11:53 by Graham Healy . Last Modified 11 Oct 2018 08:58
Documents

Full text available as:

[thumbnail of mmm 2016 paper into llcns]
Preview
PDF (mmm 2016 paper into llcns) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record