@inproceedings{1d8e64859f1446af9e229f382a44e92f,
title = "A hybrid ICA-wavelet transform for automated artefact removal in EEG-based emotion recognition",
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.",
keywords = "EEG, Emotion, Independent component analysis, Wavelet",
author = "Bigirimana, {A. D.} and N. Siddique and D. Coyle",
year = "2017",
month = feb,
day = "9",
doi = "10.1109/SMC.2016.7844928",
language = "English",
isbn = "978-1-5090-1898-7",
series = "IEEE International Conference on Systems, Man, and Cybernetics, SMC ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4429--4434",
booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings",
address = "United States",
note = "2016 IEEE International Conference on Systems, Man, and Cybernetics ; Conference date: 09-10-2016 Through 12-10-2016",
}