Paper Info
Title
Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble.
Abstract
Among various physiological signal acquisition methods for the study of the human brain, EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive, and accurate way of capturing brain signals in multiple channels at fine temporal resolution. We propose an ensemble learning algorithm for automatically computing the most discriminative subset of EEG channels for internal emotion recognition. Our method describes an EEG channel using kernel-based representations computed from the training EEG recordings. For ensemble learning, we formulate a graph embedding linear discriminant objective function using the kernel representations. The objective function is efficiently solved via sparse non-negative principal component analysis and the final classifier is learned using the sparse projection coefficients. Our algorithm is useful in reducing the amount of data while improving computational efficiency and classification accuracy at the same time. The experiments on publicly available EEG dataset demonstrate the superiority of the proposed algorithm over the compared methods.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2904400
IEEE ACCESS
Keywords
Field
DocType
Multiple channel EEG,emotion recognition,linear discriminant analysis,sparse PCA
Kernel (linear algebra),Pattern recognition,Computer science,Graph embedding,Emotion classification,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Ensemble learning,Discriminative model,Electroencephalography,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Habib Ullah1265.59
Muhammad Uzair201.01
Arif Mahmood338733.58
Mohib Ullah4228.82
Sultan Daud Khan520.75
Faouzi Alaya Cheikh616838.47