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Entropy 2018, 20(5), 386; https://doi.org/10.3390/e20050386

Identification of Auditory Object-Specific Attention from Single-Trial Electroencephalogram Signals via Entropy Measures and Machine Learning

Key Laboratory of Shenzhen Internet of Things Terminal Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China
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Received: 17 April 2018 / Revised: 16 May 2018 / Accepted: 16 May 2018 / Published: 21 May 2018
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Abstract

Existing research has revealed that auditory attention can be tracked from ongoing electroencephalography (EEG) signals. The aim of this novel study was to investigate the identification of peoples’ attention to a specific auditory object from single-trial EEG signals via entropy measures and machine learning. Approximate entropy (ApEn), sample entropy (SampEn), composite multiscale entropy (CmpMSE) and fuzzy entropy (FuzzyEn) were used to extract the informative features of EEG signals under three kinds of auditory object-specific attention (Rest, Auditory Object1 Attention (AOA1) and Auditory Object2 Attention (AOA2)). The linear discriminant analysis and support vector machine (SVM), were used to construct two auditory attention classifiers. The statistical results of entropy measures indicated that there were significant differences in the values of ApEn, SampEn, CmpMSE and FuzzyEn between Rest, AOA1 and AOA2. For the SVM-based auditory attention classifier, the auditory object-specific attention of Rest, AOA1 and AOA2 could be identified from EEG signals using ApEn, SampEn, CmpMSE and FuzzyEn as features and the identification rates were significantly different from chance level. The optimal identification was achieved by the SVM-based auditory attention classifier using CmpMSE with the scale factor τ = 10. This study demonstrated a novel solution to identify the auditory object-specific attention from single-trial EEG signals without the need to access the auditory stimulus. View Full-Text
Keywords: auditory attention; entropy measure; linear discriminant analysis (LDA); support vector machine (SVM); auditory attention classifier; electroencephalography (EEG) auditory attention; entropy measure; linear discriminant analysis (LDA); support vector machine (SVM); auditory attention classifier; electroencephalography (EEG)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Lu, Y.; Wang, M.; Zhang, Q.; Han, Y. Identification of Auditory Object-Specific Attention from Single-Trial Electroencephalogram Signals via Entropy Measures and Machine Learning. Entropy 2018, 20, 386.

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