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Exploratory multimodal data analysis with standard multimedia player multimedia containers: a feasible solution to make multimodal research data accessible to the broad audience

Title	Exploratory multimodal data analysis with standard multimedia player multimedia containers: a feasible solution to make multimodal research data accessible to the broad audience
Author	Schoning, J., Gert, A. L., Açık, Alper, Kietzmann, T. C., Heidemann, G., Konig, P.
Publication Date:	2017
Publication Place	- SCITEPRESS, Science and Technology Publications
Subject	Multimodal data analysis, Visualization, Sonification, Gaze data, EEG data
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-989-758-225-7
Record ID	4d77fc85-108a-4889-a26e-94bbbf3a7ce3
Library Location	Psychology
Date	2017
Sample Text	The analysis of multimodal data comprised of images, videos and additional recordings, such as gaze trajectories, EEG, emotional states, and heart rate is presently only feasible with custom applications. Even exploring such data requires compilation of specific applications that suit a specific dataset only. This need for specific applications arises since all corresponding data are stored in separate files in custom-made distinct data formats. Thus accessing such datasets is cumbersome and time-consuming for experts and virtually impossible for non-experts. To make multimodal research data easily shareable and accessible to a broad audience, like researchers from diverse disciplines and all other interested people, we show how multimedia containers can support the visualization and sonification of scientific data. The use of a container format allows explorative multimodal data analyses with any multimedia player as well as streaming the data via the Internet. We prototyped this approach on two datasets, both with visualization of gaze data and one with additional sonification of EEG data. In a user study, we asked expert and non-expert users about their experience during an explorative investigation of the data. Based on their statements, our prototype implementation, and the datasets, we discuss the benefit of storing multimodal data, including the corresponding videos or images, in a single multimedia container. In conclusion, we summarize what is necessary for having multimedia containers as a standard for storing multimodal data and give an outlook on how artificial networks can be trained on such standardized containers.
Cilt	4

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Exploratory multimodal data analysis with standard multimedia player multimedia containers: a feasible solution to make multimodal research data accessible to the broad audience

Author Schoning, J., Gert, A. L., Açık, Alper, Kietzmann, T. C., Heidemann, G., Konig, P.

Publication Date 2017

Publication Place - SCITEPRESS, Science and Technology Publications

Subject Multimodal data analysis, Visualization, Sonification, Gaze data, EEG data

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-989-758-225-7

Record ID 4d77fc85-108a-4889-a26e-94bbbf3a7ce3

Library Location Psychology

Date 2017

Sample Text The analysis of multimodal data comprised of images, videos and additional recordings, such as gaze trajectories, EEG, emotional states, and heart rate is presently only feasible with custom applications. Even exploring such data requires compilation of specific applications that suit a specific dataset only. This need for specific applications arises since all corresponding data are stored in separate files in custom-made distinct data formats. Thus accessing such datasets is cumbersome and time-consuming for experts and virtually impossible for non-experts. To make multimodal research data easily shareable and accessible to a broad audience, like researchers from diverse disciplines and all other interested people, we show how multimedia containers can support the visualization and sonification of scientific data. The use of a container format allows explorative multimodal data analyses with any multimedia player as well as streaming the data via the Internet. We prototyped this approach on two datasets, both with visualization of gaze data and one with additional sonification of EEG data. In a user study, we asked expert and non-expert users about their experience during an explorative investigation of the data. Based on their statements, our prototype implementation, and the datasets, we discuss the benefit of storing multimodal data, including the corresponding videos or images, in a single multimedia container. In conclusion, we summarize what is necessary for having multimedia containers as a standard for storing multimodal data and give an outlook on how artificial networks can be trained on such standardized containers.

Cilt 4