Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble

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 lear...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.40144-40153
Main Authors: Ullah, Habib, Uzair, Muhammad, Mahmood, Arif, Ullah, Mohib, Khan, Sultan Daud, Cheikh, Faouzi Alaya
Format: Article
Language:English
Subjects:
Algorithms
Brain
Channels
Electroencephalography
Emotion recognition
Emotions
Feature extraction
Kernel
Kernels
linear discriminant analysis
Linear programming
Machine learning
Multiple channel EEG
Principal components analysis
Representations
Signal classification
sparse PCA
Support vector machines
Temporal resolution
Training
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2904400