Evaluating ANN efficiency in recognizing EEG and eye-tracking evoked potentials in visual-game-events
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
Evaluating ANN efficiency in recognizing EEG and eye-tracking evoked potentials in visual-game-events. / Wulff-Jensen, Andreas; Bruni, Luis Emilio.
Advances in Neuroergonomics and Cognitive Engineering: Proceedings of the AHFE 2017 International Conference on Neuroergonomics and Cognitive Engineering, July 17–21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA. red. / Carryl Baldwin. Cham : Springer, 2018. s. 262-274.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Evaluating ANN efficiency in recognizing EEG and eye-tracking evoked potentials in visual-game-events
AU - Wulff-Jensen, Andreas
AU - Bruni, Luis Emilio
N1 - (Ekstern)
PY - 2018
Y1 - 2018
N2 - EEG and Eye-tracking signals have customarily been analyzed and inspected visually in order to be correlated to the controlled stimuli. This process has proven to yield valid results as long as the stimuli of the experiment are under complete control (e.g.: the order of presentation). In this study, we have recorded the subject’s electroencephalogram and eye-tracking data while they were exposed to a 2D platform game. In the game we had control over the design of each level by choosing the diversity of actions (i.e. events) afforded to the player. However we had no control over the order in which these actions were undertaken. The psychophysiological signals were synchronized to these game events and used to train and test an artificial neural network in order to evaluate how efficiently such a tool can help us in establishing the correlation, and therefore differentiating among the different categories of events. The highest average accuracies were between 60.25%–72.07%, hinting that it is feasible to recognize reactions to complex uncontrolled stimuli, like game events, using artificial neural networks.
AB - EEG and Eye-tracking signals have customarily been analyzed and inspected visually in order to be correlated to the controlled stimuli. This process has proven to yield valid results as long as the stimuli of the experiment are under complete control (e.g.: the order of presentation). In this study, we have recorded the subject’s electroencephalogram and eye-tracking data while they were exposed to a 2D platform game. In the game we had control over the design of each level by choosing the diversity of actions (i.e. events) afforded to the player. However we had no control over the order in which these actions were undertaken. The psychophysiological signals were synchronized to these game events and used to train and test an artificial neural network in order to evaluate how efficiently such a tool can help us in establishing the correlation, and therefore differentiating among the different categories of events. The highest average accuracies were between 60.25%–72.07%, hinting that it is feasible to recognize reactions to complex uncontrolled stimuli, like game events, using artificial neural networks.
KW - Faculty of Science
KW - Artificial neural network
KW - Machine learning
KW - Electroencephalogram
KW - Eye-tracking
KW - Games
KW - Pupillometry
KW - Game events
KW - Psychophysiology
U2 - 10.1007/978-3-319-60642-2_25
DO - 10.1007/978-3-319-60642-2_25
M3 - Article in proceedings
SN - 978-3-319-60641-5
SP - 262
EP - 274
BT - Advances in Neuroergonomics and Cognitive Engineering
A2 - Baldwin, Carryl
PB - Springer
CY - Cham
ER -
ID: 315572724