Emotion Classification by EEG Signal Generated by Brain using Discrete Wavelet Transform and Artificial Neural Network Backpropagation with Classical Music Stimulus

Aditya Dimas
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引用次数: 2

Abstract

People feel different emotions when listening to music on certain levels. Such feelings occur because the music stimuli causing reduced or increased brain activity and producing brainwave with specific characteristics. Results of research indicated that classical piano music can influence one’s emotional intelligent. By using Electroenchephalography (EEG) as a brainwave recording instrument, we can assess the effect of stimulation on the emotions generated through brain activity. This study aimed at developing a method that defines the effect of sound to brain activity using an EEG signal that can be used to identify one's emotion based on classical piano music stimulus reaction. Based on its frequency, this signal was the classified using DWT. To train Artificial Neural Network, some features were taken from the signal. This ANN research was carried out using the process of backpropagation
古典音乐刺激下脑电信号离散小波变换与人工神经网络反向传播的情绪分类
人们在听不同程度的音乐时会有不同的情绪。这种感觉的产生是因为音乐刺激导致大脑活动减少或增加,并产生具有特定特征的脑电波。研究结果表明,古典钢琴音乐可以影响一个人的情商。利用脑电图(EEG)作为脑电波记录工具,我们可以评估刺激对通过大脑活动产生的情绪的影响。这项研究旨在开发一种方法,利用脑电图信号来定义声音对大脑活动的影响,这种信号可以用来识别基于古典钢琴音乐刺激反应的人的情绪。根据信号的频率,采用小波变换对信号进行分类。为了训练人工神经网络,从信号中提取一些特征。本人工神经网络研究采用反向传播过程进行
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