A multimodal dataset for authoring and editing multimedia content: the MAMEM project

S. Nikolopoulos, P.C. Petrantonakis, K. Georgiadis, F. Kalaganis, G. Liaros, I. Lazarou, K. Adam, A. Papazoglou-Chalikias, E. Chatzilari, V.P. Oikonomou, C. Kumar, R. Menges, S. Staab, D. Müller, K. Sengupta, S. Bostantjopoulou, Z. Katsarou, G. Zeilig, M. Plotnik, A. GotliebR. Kizoni, S. Fountoukidou, J.R.C. Ham, D. Athanasiou, A. Mariakaki, D. Comanducci, E. Sabatini, W. Nistico, M. Plank, I. Kompatsiaris

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
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We present a dataset that combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals collected from 34 individuals (18 able-bodied and 16 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. The presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.

Original languageEnglish
Pages (from-to)1048-1056
Number of pages9
JournalData in Brief
Publication statusPublished - 1 Dec 2017


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