Electroencephalographic based hearing identification using back-propagation algorithm

Electroencephalographic (EEG) based hearing identification using artificial intelligent is an application between human's cognitive ability (hearing), EEG technology and artificial intelligent. EEG signals which are produced when a subject listen to an audible sounds with particular frequency w...

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Vydáno v:2009 IEEE Toronto International Conference Science and Technology for Humanity s. 991 - 995
Hlavní autoři: Sudirman, R, Seow, S C
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2009
Témata:
Artificial intelligence
artificial intelligent
Artificial neural networks
Auditory system
back-propagation neural network
Biological neural networks
Electroencephalographic
Electroencephalography
Feedforward neural networks
Feedforward systems
Frequency
hearing threshold
Neural networks
Signal analysis
ISBN:9781424438778, 1424438772
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Abstract Electroencephalographic (EEG) based hearing identification using artificial intelligent is an application between human's cognitive ability (hearing), EEG technology and artificial intelligent. EEG signals which are produced when a subject listen to an audible sounds with particular frequency will be recorded using Neurofax EEG-9200 device for further analysis. The EEG signals are the sources for this research; used to train a 21 layers feed-forward back-propagation neural network (NN) in order to recognize the patterns of the brain wave. The EEG signals are analyzed using Fast Fourier Transform (FFT) and filtering techniques available in Matlab. Furthermore, the well trained network can recognise the brain signal effectively. A graphic user interface (GUI) has been developed to display the digitalised brain signal and identification result. The result showed that the NN algorithm was able to process the EEG data to identify the sound frequency perceived by the subjects.
AbstractList Electroencephalographic (EEG) based hearing identification using artificial intelligent is an application between human's cognitive ability (hearing), EEG technology and artificial intelligent. EEG signals which are produced when a subject listen to an audible sounds with particular frequency will be recorded using Neurofax EEG-9200 device for further analysis. The EEG signals are the sources for this research; used to train a 21 layers feed-forward back-propagation neural network (NN) in order to recognize the patterns of the brain wave. The EEG signals are analyzed using Fast Fourier Transform (FFT) and filtering techniques available in Matlab. Furthermore, the well trained network can recognise the brain signal effectively. A graphic user interface (GUI) has been developed to display the digitalised brain signal and identification result. The result showed that the NN algorithm was able to process the EEG data to identify the sound frequency perceived by the subjects.
Author Sudirman, R
Seow, S C
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  organization: Fac. of Electr. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia
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  surname: Seow
  fullname: Seow, S C
  organization: Fac. of Electr. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia
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SubjectTerms Artificial intelligence
artificial intelligent
Artificial neural networks
Auditory system
back-propagation neural network
Biological neural networks
Electroencephalographic
Electroencephalography
Feedforward neural networks
Feedforward systems
Frequency
hearing threshold
Neural networks
Signal analysis
Title Electroencephalographic based hearing identification using back-propagation algorithm
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