Multi-layer Stacking-based Emotion Recognition using Data Fusion Strategy
subject: Electroencephalograph (eeg); Emotion recognition; Feature selection; Linear regression based correlation coefficient; Machine learning techniques; Multi-layer stacking model
description: Electroencephalography (EEG), or brain waves, is a commonly utilized bio signal in emotion detection because it has been discovered that the data recorded from the brain seems to have a connection between motions and physiological effects. This paper is based on the feature selection strategy by using the data fusion technique from the same source of EEG Brainwave Dataset for Classification. The multi-layer Stacking Classifier with two different layers of machine learning techniques was introduced in this approach to concurrently learn the feature and distinguish the emotion of pure EEG signals states in positive, neutral and negative states. First layer of stacking includes the support vector classifier and Random Forest, and the second layer of stacking includes multilayer perceptron and Nu-support vector classifiers. Features are selected based on a Linear Regression based correlation coefficient (LR-CC) score with a different range like n1, n2,n3,n4 a, for d1 used n1 and n2 dataset,for d2 dataset, combined dataset of n3 and n4 are used and developed a new dataset d3 which is the combination of d1 and d2 by using the feature selection strategy which results in 997 features out of 2548 features of the EEG Brainwave dataset with a classification accuracy of emotion recognition 98.75%, which is comparable to many state-of-the-art techniques. It has been established some scientific groundwork for using data fusion strategy in emotion recognition. 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved.
creator: Tahseen S.; Danti A.
source: International Journal of Advanced Computer Science and Applications, Vol-13, No. 6, pp. 433-442.
publisher: Science and Information Organization
date: 2022-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 2158107X
format: Online
language: English
type: Article
coverage: Tahseen S., Dept. of Computer Science and Engineering, Christ University, Bangalore, India; Danti A., Dept. of Computer Science and Engineering, Christ University, Bangalore, India