Modeling the mental differentiation task with EEG

Authors:
Tan Vo;Tom Gedeon;Dat Tran
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, Canberra, ACT, Australia;Research School of Computer Science, The Australian National University, Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, Canberra, ACT, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

Differentiation in human beings is the act of perceiving the difference in or between objects. In other words, it is the mental process taking place to discriminate one thing from others, a common task performed by a person on a very regular basis. Making such differentiations, small or large, easy or hard, still requires a combination of cognitive processes to occur across various parts of the human brain. In this paper, an EEG-based BCI experiment was organized to study the detection of such cognitive processes. Utilizing a machine learning tool, Artificial Neural Networks, to aid in analyzing the acquired dataset, a high correct classification rate was achieved, confirming that it is possible to computationally detect these differentiation activities from EEG signals.