University of Hertfordshire

By the same authors

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

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

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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
CountryBulgaria
CityVarna
Period14/09/1716/09/17

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.

ID: 19523134