Detection of Anxiety Expression From EEG Analysys Using Support Vector Machine

Wan Junaidee bin Wan Hamat, Harris Majdi bin Ishak, Kotaro Hashikura, Takaaki Suzuki, Kou Yamada

Abstract


Support Vector Machine (SVMs) have been extensively researched in data mining and machine learning communities for the last decade and actively applied to application in various domains. SVMs are typically used for learning classification, regression and ranking function. Two specials properties of SVMs are that SVMs achieve high generalization by maximizing the margin and support an efficient learning of nonlinear functions by kernel trick. In this paper, we present how to clarify when we feel anxiety by using SVM technique to estimate the condition of user.


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