Abstract
Detection of unanticipated and rare events refers to a process of identifying an occasional target (oddball) stimulus from a regular trail of standard stimuli based on brain wave signals. It is the premise of human event-related potential (ERP) applications, a significant research topic in brain computer interfaces. The focus of this paper is to investigate whether unanticipated and rare visual and auditory events are detectable from EEG signals. In order to achieve this, an exploratory experiment is conducted. A novel pairwise comparison neural network approach to detect those unanticipated and rare visual and auditory events from EEG signals is introduced. Results indicate that the change in EEG signals caused by unanticipated rare events is detectable; a piece of finding that opens opportunities for ERP-based applications.
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Acknowledgment
The authors wish to thank Ms Xuejie Liu for implementing the directional sound system. The Human Research Ethics Advisory Panel of University of New South Wales approved the experimental protocol under Approval HC15806.
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Wang, M., Abbass, H.A., Hu, J., Merrick, K. (2016). Detecting Rare Visual and Auditory Events from EEG Using Pairwise-Comparison Neural Networks. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_9
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DOI: https://doi.org/10.1007/978-3-319-49685-6_9
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