A Study on Analysis of Bio-Signals for Basic Emotions Classification: Recognition Using Machine Learning Algorithms

Abstract

The most crucial feature of human computer interaction is computers and computer-based applications to infer the emotional states of humans or others human agents based on covert and/or overt signals of those emotional states. In emotion recognition, bio-signals reflect sequences of neural activity induced by emotional events and also, have many technical advantages. The aim of this study is to classify six emotions (joy, sadness, anger, fear, surprise, and neutral) that human have often experienced in real life from multi-channel bio-signals using machine learning algorithms. We have measured physiological responses of three-hundred participants for acquisition of bio-signals such as electrodermal activity, electrocardiograph, skin temperature, and photoplethysmograph during six emotions induction. Also, for emotion classification, we have extracted eighteen features from the signals and performed emotion classification using five algorithms, linear discriminant analysis, Naïve Bayes, classification and regression tree, selforganization map and support vector machine. The used algorithms were evaluated by only training, 10-fold crossvalidation and repeated random sub-sampling validation. We have obtained recognition accuracy from 42.4 to 100% for only training and 39.2 to 53.9% for testing. Also, the result for testing showed that an accuracy of emotion recognition by Naïve Bayes and linear discriminant analysis were highest (53.9%, 52.7%) and was lowest by support vector machine (39.2%). This means that Naïve Bayes is the best emotion recognition algorithm for basic emotions. To apply to real system, we have to discuss in the view point of testing and this means that it needs to apply various methodologies for the accuracy improvement of emotion recognition in the future analysis.

Driver’s stress detection using Skin Potential Response signals

“…Several methods are employed to train and classify physiological signals features. Such implementations can be found in [56] for linear discriminant analysis (LDA), Naïve Bayes, classification and regression tree (CART), self-organization map (SOMs), and support vector machine (SVM) or in [104] for Naive Bayes, Support Vector Machine (SVM) with linear and RBF kernel. In [76], a comparison of different classifiers concluded that KNN and SVM outperform Discriminant Analysis and Classification Trees.…”

Multimodal Emotion Recognition Using Deep Networks

“…Here, the features extracted mainly consisted on holistic representations (discrete Fourier coefficients, PCA projections of the face), parametric flow models and facial landmarks [4]. More recently, other modalities such as body gestures, biosignals and others have been started to be used [5].…”