Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

Muhammad Fauzan Ar Rasyid, Esmeralda C. Djamal

Abstract


One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing is based on neuropsychology, which is sourced from the human brain through electrical activity signals recorded by Electroencephalogram. Usually, Neuropsychology consists of emotions, attention, and concentration. This research proposed the Wavelet method and Recurrent Neural Networks to measure the emotional and attention variable of neuropsychology in real-time every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 4%.

Keywords


neuromarketing; EEG signal; wavelet extraction; recurrent neural networks

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