Parallel artificial neural network approaches for detecting the behaviour of eye movement using CUDA software on heterogeneous CPU-GPU systems
Eye movement behaviour is related to human brain activation either during asleep or awake. The aim of this paper is to measure the three types of eye movement by using the data classification of electroencephalogram (EEG) signals. It will be illustrated and train using the artificial neural network...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/71282/1/NormaAlias2016_Parallelartificialneuralnetworkapproaches.pdf http://eprints.utm.my/id/eprint/71282/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006043239&doi=10.11113%2fjt.v78.10145&partnerID=40&md5=0f4c604191d735fc2630403f1dc49ae1 |
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| Summary: | Eye movement behaviour is related to human brain activation either during asleep or awake. The aim of this paper is to measure the three types of eye movement by using the data classification of electroencephalogram (EEG) signals. It will be illustrated and train using the artificial neural network (ANN) method, in which the measurement of eye movement is based on eye blinks close and open, moves to the left and right as well as eye movement upwards and downwards. The integrated of ANN with EEG digital data signals is to train the large-scale digital data and thus predict the eye movement behaviour with stress activity. Since this study is using large-scale digital data, the parallelization of integrated ANN with EEG signals has been implemented on Compute Unified Device Architecture (CUDA) supported by heterogeneous CPU-GPU systems. The real data set from eye therapy industry, IC Herbz Sdn Bhd was carried out in order to validate and simulate the eye movement behaviour. Parallel performance analyses can be captured based on execution time, speedup, efficiency, and computational complexity. |
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