Evaluation of cepstral coefficients as features in EEG-based recognition of emotional States

Firgan Feradov, Iosif Mporas, Todor Ganchev

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    The study of physiological signals and the evaluation of their features are of great importance for the automated emotion detection, as these are directly connected with the successful modelling and classification of the states of interest. In the presented work, we present an evaluation of the appropriateness of LFCC and the logarithmic energy of signals as features for automated recognition of negative emotional states in terms of recognition accuracy. In particular, three sets of features are compared – features computed after frame-level segmentation of the signal; features computed after averaging of frame level descriptors; and features extracted from an entire EEG recording. The performance of the extracted features is evaluated using C4.5 classifier for 10, 15, 20, 30, 45, and 60 filters.

    Original languageEnglish
    Title of host publicationProceedings of the 2nd International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2017
    EditorsSergey Kovalev, Andrey Sukhanov, Margreta Vasileva, Valery Tarassov, Vaclav Snasel, Ajith Abraham
    PublisherSpringer Verlag
    Pages504-511
    Number of pages8
    ISBN (Print)9783319683201
    DOIs
    Publication statusPublished - 1 Jan 2018
    Event2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017 - Varna, Bulgaria
    Duration: 14 Sep 201716 Sep 2017

    Publication series

    NameAdvances in Intelligent Systems and Computing
    Volume679
    ISSN (Print)2194-5357

    Conference

    Conference2nd International Conference on Intelligent Information Technologies for Industry, IITI 2017
    Country/TerritoryBulgaria
    CityVarna
    Period14/09/1716/09/17

    Keywords

    • Cepstral coefficients
    • EEG
    • Emotion recognition
    • LFCC features

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