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This otariid species, endemic to Australia, is listed as vulnerable under the Australian Environmental Protection and Biodiversity Conservation Act 1999 (Australian Government, 1999) and as endangered by the International Union for Conservation of Nature (IUCN) (Goldsworthy & Gales, 2015). Breeding colonies of the Australian sea lion can only be found on the south and west coasts of Australia, and numbers are declining. Mother-pup recognition in large breeding colonies is mediated primarily through acoustic cues. Any auditory impairment of a breeding sea lion could have knock-on effects in terms of nursing and, ultimately, breeding success. While by-catch in gill-net and trap fisheries is suspected to be the main threat (Goldsworthy & Gales, 2008), effects induced by excessive exposure to anthropogenic underwater sound (such as seismic exploration, underwater explosions, and pile driving for port construction; see Wyatt, 2008) might also cause changes in the distribution or abundance of this species.

In this paper, we focus on identifying the alertness state of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. A supervised classification approach is adopted, where subjects were advised to indicate their alertness states in specified time instances. Two sets of features are considered here to represent the recorded data. The first is based on the wavelet transform of the background EEG, while the second is obtained from the peaks of the CAEP responses. The rational behind using the second feature set is to evaluate the relationship between CAEP responses and alertness levels. Obtained results suggest that the CAEP-based features are very comparable, in terms of classification accuracy, to the well-established wavelet-based features of EEG signals (79% compared to 80%). The findings of this paper will contribute towards a better understanding of CAEP responses at the different alertness states. © 2013 IEEE.

In this paper, we analyze the EEG rhythms of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. Investigation of the importance of the different EEG rhythms in terms of their capability in differentiating between the different alertness states when considering 64 channel EEG montage is conducted. This is followed by considering subsets that contain 2, 3, 4 as well as all 5 EEG rhythms. Finally, a feature subset selection method based on differential evolution (DE) that has particularly been proposed to deal with multi-channel signals is used to search for the best subset of EEG rhythms for the various channels. © 2012 Springer-Verlag.

In this paper, we analyze the EEG rhythms of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. Investigation of the importance of the different EEG rhythms in terms of their capability in differentiating between the different alertness states when considering 64 channel EEG montage is conducted. This is followed by considering subsets that contain 2, 3, 4 as well as all 5 EEG rhythms. Finally, a feature subset selection method based on differential evolution (DE) that has particularly been proposed to deal with multi-channel signals is used to search for the best subset of EEG rhythms for the various channels. © 2012 Springer-Verlag.