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Cited by 55EEG and Brain-Computer InterfacesEmotion and Mood RecognitionNeuroscience and Music Perception

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A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

Open access
EskandariNasab, MohammadReza, Raeisi, Zahra, Lashaki, Reza Ahmadi, Najafi, Hamidreza·2024·Scientific Reports
DOI 10.1038/s41598-024-58886-yPMC PMC11024110
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Abstract

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

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Crossref · 10.1038/s41598-024-58886-y

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Figures

Figure 1 The proposed AAD method based on microstate and recurrence quantification analysis.
Figure 1 The proposed AAD method based on microstate and recurrence quantification analysis.CC BY · PubMed Central
Figure 2 Schematic flowchart of the EEG microstate analysis using MNE. The GFP of each sampling point is calculated and all topographic maps at the local GFP maxima are obtained. The K-means clustering analysis method is used to analyze topographic maps to obtain optimal microstate classes. At the bottom, microstate temporal sequences are obtained by fitting microstate classes back to complete EEG data.
Figure 2 Schematic flowchart of the EEG microstate analysis using MNE. The GFP of each sampling point is calculated and all topographic maps at the local GFP maxima are obtained. The K-means clustering analysis method is used to analyze topographic maps to obtain optimal microstate classes. At the bottom, microstate temporal sequences are obtained by fitting microstate classes back to complete EEG data.CC BY · PubMed Central
Figure 3 Changes in occurrence values of microstate features depend on each subject of DTU database and performance. Boxplots represent the first quantile, third quantile, and median values.
Figure 3 Changes in occurrence values of microstate features depend on each subject of DTU database and performance. Boxplots represent the first quantile, third quantile, and median values.CC BY · PubMed Central
Figure 4 Example of recurrence plots (RP) with 12 different scales for the GFP extracted from EEG of DTU database.
Figure 4 Example of recurrence plots (RP) with 12 different scales for the GFP extracted from EEG of DTU database.CC BY · PubMed Central
Figure 5 The block diagram of the proposed GCQL to improve estimating the value function.
Figure 5 The block diagram of the proposed GCQL to improve estimating the value function.CC BY · PubMed Central
Figure 6 AAD performance only with MS feature vectors on DTU and KUL databases.
Figure 6 AAD performance only with MS feature vectors on DTU and KUL databases.CC BY · PubMed Central
Figure 7 AAD performance only with RP feature vectors among all subjects on DTU and KUL databases.
Figure 7 AAD performance only with RP feature vectors among all subjects on DTU and KUL databases.CC BY · PubMed Central
Figure 8 Performance of the proposed GCQL-AAD using multivariate feature (i.e., “mean GFP + RR”) and GCQL classifier for different EEG segments on DTU and KUL databases.
Figure 8 Performance of the proposed GCQL-AAD using multivariate feature (i.e., “mean GFP + RR”) and GCQL classifier for different EEG segments on DTU and KUL databases.CC BY · PubMed Central
Figure 9 Comparison of the proposed AAD method (purple) with the others including “O’Sullivan et al16. (blue), Lu et al25. (light blue), Ciccarelli et al20. (orange), Geirnaert et al26. (red), Zakeri et al27. (yellow), Cai et al56. (neutral), and Niu et al87. (green) in terms of accuracy.
Figure 9 Comparison of the proposed AAD method (purple) with the others including “O’Sullivan et al16. (blue), Lu et al25. (light blue), Ciccarelli et al20. (orange), Geirnaert et al26. (red), Zakeri et al27. (yellow), Cai et al56. (neutral), and Niu et al87. (green) in terms of accuracy.CC BY · PubMed Central

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Citations

Cites

Show all 79 references

Research works printed but not linked: 12

  1. D. E. Broadbent, Perception and communication. Elsevier, (2013).
  2. Kallenberg, M., Desain, P. & Gielen, S. Auditory selective attention as a method for a brain computer interface. Nijmegen CNS. 2(1), 1–6 (2006).
  3. Das, N., Zegers, J., Francart, T. & Bertrand, A. EEG-informed speaker extraction from noisy recordings in neuro-steered hearing aids: Linear versus deep learning methods. BioRxiv https://doi.org/10.1101/2020.01.22.915181 (2020). · DOI
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  6. O’sullivan, J. A. et al. Attentional selection in a cocktail party environment can be decoded from single-trial EEG. J. Cerebral Cortex 25(7), 1697–1706 (2015). · DOI
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  8. Wong, D. D. et al. A comparison of regularization methods in forward and backward models for auditory attention decoding. J. Front. Neurosci. 12, 352049 (2018).
  9. Ciccarelli, G. et al. Comparison of two-talker attention decoding from EEG with nonlinear neural networks and linear methods. J. Sci. Rep. 9(1), 11538 (2019). · DOI
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Metadata

Authors
EskandariNasab, M., Raeisi, Z., Lashaki, R. A., & Najafi, H.
Affiliations
  • Utah State University (US)
  • University of Tabriz (IR)
  • Iran University of Science and Technology (IR)
Corresponding author
EskandariNasab, MohammadReza
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-04-17
Volume
14
Issue
1
Pages
8861-8861
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-024-58886-y
PMC
PMC11024110
OpenAlex
W4394881920
Cited by
55
References
79
Citations by year
2026: 8 · 2025: 20 · 2024: 27
FWCI
13.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
Keywords
auditory attention detection, EEG microstates, recurrence quantification analysis, gated recurrent unit, convolutional neural networks, feature selection, competing talkers, brain state dynamics, reinforcement learning
MeSH terms
Machine Learning, Attention, Brain, Brain Mapping, Electroencephalography, Humans, Neural Networks, Computer

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