Examining Wavelet Transform Performance on ETG Signal to Eliminate Noise

- The aim of this study was to investigate the performance of wavelet transform on ETG signal to eliminate noise. This research is aimed at improving the method of recognizing inner emotions. The proposed new method is an automated method for classifying emotions using signals (EDA). Temporal analysis of the acquired frequency that provides a space of characteristics, based on which different emotions can be identified. For this purpose, the complex wavelet function (C-Morlet) is applied to the recorded EDA signals. The data set used in this study is a set of multifaceted recordings of social and communication behaviors as well as EDA records. The data set is interpreted to extract a time sequence corresponding to the three main emotions “happiness”, “boredom” and “acceptance”. The simulation results show that the level 3 violet sym4 conversion removes noise from the ETG signal well and increases the signal to noise. The results of the simulation using this method are more efficient than other methods and reduce more noise.