Emotional profiling through supervised machine learning of interrupted EEG interpolation

Abstract

It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%.

An Efficient K-Means Method Based on Centroid Handling for the Similarity Estimation

“…Standard data mining techniques have been used widely in the distance estimation tasks [13]. The concrete structure of any data mining exploration relies on the appropriate data representation in terms of the data scenic approach for the collaborative approach of the data centric approach in terms of data exploration and data recognition [14][15][16].…”