A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis
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 multichanne...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-58886-y |
| _version_ | 1797199538027495424 |
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| author | MohammadReza EskandariNasab Zahra Raeisi Reza Ahmadi Lashaki Hamidreza Najafi |
| author_facet | MohammadReza EskandariNasab Zahra Raeisi Reza Ahmadi Lashaki Hamidreza Najafi |
| author_sort | MohammadReza EskandariNasab |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T07:17:20Z |
| format | Article |
| id | doaj.art-4d30353584a14cccbb60d21573244b06 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T07:17:20Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4d30353584a14cccbb60d21573244b062024-04-21T11:14:55ZengNature PortfolioScientific Reports2045-23222024-04-0114111810.1038/s41598-024-58886-yA GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysisMohammadReza EskandariNasab0Zahra Raeisi1Reza Ahmadi Lashaki2Hamidreza Najafi3College of Science, Utah State UniversityDepartment of Computer Science, University of Fairleigh DickinsonDepartment of Computer Engineering, Faculty of Electrical and Computer Engineering, University of TabrizBiomedical Engineering Department, School of Electrical Engineering, Iran University of Science and TechnologyAbstract 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.https://doi.org/10.1038/s41598-024-58886-yAuditory attention detectionGRU–CNNEEGMicrostate analysisMachine learning algorithmsMultivariate features |
| spellingShingle | MohammadReza EskandariNasab Zahra Raeisi Reza Ahmadi Lashaki Hamidreza Najafi A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis Scientific Reports Auditory attention detection GRU–CNN EEG Microstate analysis Machine learning algorithms Multivariate features |
| title | A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis |
| title_full | A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis |
| title_fullStr | A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis |
| title_full_unstemmed | A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis |
| title_short | A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis |
| title_sort | gru cnn model for auditory attention detection using microstate and recurrence quantification analysis |
| topic | Auditory attention detection GRU–CNN EEG Microstate analysis Machine learning algorithms Multivariate features |
| url | https://doi.org/10.1038/s41598-024-58886-y |
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