Internal Emotion Classification Using EEG Signal With Sparse Discriminative Ensemble

• Published in: IEEE access Vol. 7; pp. 40144 - 40153
• Main Authors: Ullah, Habib, Uzair, Muhammad, Mahmood, Arif, Ullah, Mohib, Khan, Sultan Daud, Cheikh, Faouzi Alaya
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2904400

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.