Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-6087
Main Title: Prediction of difficulty levels in video games from ongoing EEG
Author(s): Neumann, Laura
Schultze-Kraft, Matthias
Dähne, Sven
Blankertz, Benjamin
Type: Book Part
Language Code: en
Is Part Of: 10.1007/978-3-319-57753-1
Abstract: Real-time assessment of mental workload from EEG plays an important role in enhancing symbiotic interaction of human operators in immersive environments. In this study we thus aimed at predicting the difficulty level of a video game a person is playing at a particular moment from the ongoing EEG activity. Therefore, we made use of power modulations in the theta (4–7 Hz) and alpha (8–13 Hz) frequency bands of the EEG which are known to reflect cognitive workload. Since the goal was to predict from multiple difficulty levels, established binary classification approaches are futile. Here, we employ a novel spatial filtering method (SPoC) that finds spatial filters such that their corresponding bandpower dynamics maximally covary with a given target variable, in this case the difficulty level. EEG was recorded from 6 participants playing a modified Tetris game at 10 different difficulty levels. We found that our approach predicted the levels with high accuracy, yielding a mean prediction error of less than one level.
URI: http://depositonce.tu-berlin.de/handle/11303/6646
http://dx.doi.org/10.14279/depositonce-6087
Issue Date: 2017
Date Available: 28-Aug-2017
DDC Class: 004 Informatik
Subject(s): BCI
cognitive workload
video games
EEG
machine learning
spatial filtering
Sponsor/Funder: EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSee
BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
Book Title: Symbiotic Interaction
Publisher: Springer
Publisher Place: Berlin, Heidelberg
Publisher DOI: 10.1007/978-3-319-57753-1_11
Page Start: 125
Page End: 136
Series: Lecture Notes in Computer Science
Series Number: 9961
EISSN: 1611-3349
ISBN: 978-3-319-57753-1
ISSN: 0302-9743
Appears in Collections:Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 4 Elektrotechnik und Informatik » Institut für Softwaretechnik und Theoretische Informatik » Fachgebiet Neurotechnologie » Publications

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