Title :
EEG based estimation of hearing frequency perception by artificial neural networks
Author :
Paulraj, M.P. ; Yaccob, S.B. ; Adom, A.H.B. ; Subramaniam, Kamalraj ; Hema, C.R.
Author_Institution :
Sch. of Mechatron. Eng., Univ. Malaysia Perlis, Arau, Malaysia
Abstract :
Auditory evoked potentials are a type of EEG signal emanated from the scalp of the brain by an acoustical stimulus. In this paper, auditory evoked potential (AEP) signals emanated while hearing the click-sound stimuli excited at three different frequencies were recorded. Spatio-temporal features of four distinct bands were extracted from the recorded AEP signal. The extracted features were then associated to the hearing frequency perception response of an individual and neural network models for left and right ears were developed. The maximum classification accuracy of the developed neural network model in discriminating the hearing frequency perception response of a person has been observed as 94.5 per cent.
Keywords :
auditory evoked potentials; bioacoustics; ear; electroencephalography; feature extraction; frequency estimation; medical signal detection; medical signal processing; neural nets; signal classification; AEP signals; EEG hearing estimation; EEG signal types; acoustical stimulus; artificial neural networks; auditory evoked potentials; brain scalp; click sound stimuli excitation; ears; hearing discrimination; hearing frequency perception response; maximum classification accuracy; neural network models; spatiotemporal feature extraction; EEG; auditory evoked potential; auditory stimuli level; hearing perception; neural network;
Conference_Titel :
Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1664-4
DOI :
10.1109/IECBES.2012.6498083