Universiti Teknologi Malaysia Institutional Repository

The effect of noise removing on emotional classification

Molavi, M. and Yunus, Jasmy (2012) The effect of noise removing on emotional classification. In: 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 - Conference Proceedings. IEEE, New York, USA, pp. 485-489. ISBN 978-146731938-6

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Official URL: http://dx.doi.org/10.1109/ICCISci.2012.6297294

Abstract

This paper explains the issues of study that was designed to evaluate the effect of denoising algorithm to detect emotional expression through Electroencephalogram (EEG). This research led to classify the EEG features due to emotion which was induced by the facial expression stimulus include of happy and sad and neutral cases. Event-related potential (ERP) method was selected to probe the ability of Independent components analysis (ICA) and principal components analysis (PCA) as denoising mathematical tool which is used for data preprocessing. The features were extracted by common spatial patterns (CSP) to decrease the dimensions of data. After that extracted components was classified by support vector machine (SVM) to show the effect of noise removing on data classification. The results show that ICA could provide the most accurate result for classifying emotional states in brain activity than other methods. However, the PCA was not shown a very different and inaccurate classification results.

Item Type:Book Section
Additional Information:Indexed by Scopus
Uncontrolled Keywords:accuracy, electroencephalography, feature extraction, noise, noise reduction, principal component analysis, support vector machines
Subjects:Q Science > Q Science (General)
Divisions:Biosciences and Medical Engineering
ID Code:36045
Deposited By: Fazli Masari
Deposited On:02 Dec 2013 12:22
Last Modified:02 Feb 2017 13:59

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