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Asian Journal of Engineering, Sciences and Technology - Volume 7, Issue 1 2017
By Syed Sajjad Hussain Rizvi, Jawwad Ahmad, Muhammad Zubair Salman Zaidi
An extensive growth in EEG based systems have been observed in the recent decade. This includes treatment and support of mentally challenged persons, gaming, marketing, development of brain controlled devices etc. The performance of all these applications are primarily based on precise classification of human emotions. In this paper, an effective fractional least mean square based emotion classifier is proposed. The rigorous training and testing of the proposed classifier was performed on a benchmark video stimuli based EEG signal dataset. Moreover, the performance of the proposed classifier is also compared with the classical and most employed approach. Results prove that the proposed classifier for human emotions is far better than the most employed classification approach.