Identification of different frequency sound response from EEG signal
In recent years, a lot of research has been carried out to study on human brain response when listening to different kinds of music, as well as different quality of sound waves. High frequency component and low frequency component, either audible or inaudible, contained in a sound wave is proven to...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/18334/1/AliceKohCheeMFKE2009.pdf |
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| Summary: | In recent years, a lot of research has been carried out to study on human brain response when listening to different kinds of music, as well as different quality of sound waves. High frequency component and low frequency component, either audible or inaudible, contained in a sound wave is proven to as a cognitive factor to human acoustic system and can be shown through human brain signal. Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this project, electrical activity of human brain due to sound waves of different frequency is studied based on EEG signals. Collection of EEG signals from 5 healthy adults is done using Neurofax EEG-9100 device. Subjects are exposed to different frequency sound waves of 40 Hz, 500 Hz, 5000 Hz and 15000 Hz. The EEG signals are then processed using signal processing algorithms in Matlab, i.e. Principle Component Analysis, Discrete Wavelet Transform, Fast Fourier Transform etc. Useful information is extracted from the processing of EEG signal, and algorithm to identify the different frequency sound response from the EEG signals is developed using artificial intelligent techniques, i.e. neural network, fuzzy logic, and adaptive neuro-fuzzy system. The result from this project has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and yet different frequency sound response from EEG signal can be identified by using suitable A.I. algorithms. |
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