A Hybrid ICA-Wavelet Transform for Automated Artefact Removal in EEG-based Emotion Recognition

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Abstract

Removing artefacts from electroencephalographic (EEG) recordings normally increases their low signal-to-noise ratio and enables more reliable interpretation of brain activity. In this paper we present an evaluation of an automatic independent component analysis (ICA) procedure, a hybrid ICA - wavelet transform technique (ICA-W), for artefact removal from EEG correlated to emotional-state. Spectral and statistical features were classified with support vector machines (SVM) to assess the performance of ICA-W against the regular ICA, in terms of the accuracy of classifying emotional states from EEG. Accuracies on data from 14 subjects are reported and the results indicate that ICA-W performs better than traditional ICA in statistical and wavelet based features whilst the best overall performance is achieved when combining ICA-W with statistical features with an average accuracy across subjects of 74.11% for classifying four categories of emotion. ICA-W is therefore demonstrated to enhance EEG-based emotion recognition applications in terms of performance and ease of application.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages4429-4434
Number of pages6
ISBN (Print)978-1-5090-1897-0
DOIs
Publication statusPublished - 9 Oct 2016
Event2016 IEEE International Conference on Systems, Man, and Cybernetics - Budapest, Hungary
Duration: 9 Oct 2016 → …

Conference

Conference2016 IEEE International Conference on Systems, Man, and Cybernetics
Period9/10/16 → …

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Keywords

  • Independent component analysis
  • EEG
  • wavelet
  • emotion

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