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Real-Time Subject-Dependent EEG-Based Emotion Recognition Algorithm

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Book cover Transactions on Computational Science XXIII

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 8490))

Abstract

In this paper, we proposed a real-time subject-dependent EEG-based emotion recognition algorithm and tested it on experiments’ databases and the benchmark database DEAP. The algorithm consists of two parts: feature extraction and data classification with Support Vector Machine (SVM). Use of a Fractal Dimension feature in combination with statistical and Higher Order Crossings (HOC) features gave results with the best accuracy and with adequate computational time. The features were calculated from EEG using a sliding window. The proposed algorithm can recognize up to 8 emotions such as happy, surprised, satisfied, protected, angry, frightened, unconcerned, and sad using 4 electrodes in real time. Two experiments with audio and visual stimuli were implemented, and the Emotiv EPOC device was used to collect EEG data.

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Authors and Affiliations

  1. Fraunhofer IDM@NTU, Nanyang Technological University, Singapore

    Yisi Liu & Olga Sourina

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  1. Yisi Liu
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  2. Olga Sourina
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Editors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Marina L. Gavrilova

  2. CloudFabriQ Ltd., London, UK

    C. J. Kenneth Tan

  3. University of Yamanashi, Takeda, Japan

    Xiaoyang Mao

  4. Google Research, Palo Alto, CA, USA

    Lichan Hong

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Liu, Y., Sourina, O. (2014). Real-Time Subject-Dependent EEG-Based Emotion Recognition Algorithm. In: Gavrilova, M.L., Tan, C.J.K., Mao, X., Hong, L. (eds) Transactions on Computational Science XXIII. Lecture Notes in Computer Science, vol 8490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43790-2_11

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  • DOI: https://doi.org/10.1007/978-3-662-43790-2_11

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