ArticleScientific reports2024
A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments.Nature neuroscience · 2026Article
- Decoding selective auditory attention to musical elements in ecologically valid music listening.Scientific reports · 2026Article
- Hybrid channel attention network for auditory attention detection.Scientific reports · 2025Article
- MSRLNet: A Multi-Source Fusion and Feedback Network for EEG Feature Recognition in ADHD.Brain sciences · 2025Article
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
- Mesenchymal stromal cells in bone marrow niche of patients with multiple myeloma: a double-edged sword.Cancer cell international · 2025Review
- EEG synchronization signatures for decoding attentional states during continuous force control.Frontiers in neuroscience · 2025Article
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.Biomimetics (Basel, Switzerland) · 2024Article
- Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.Bioengineering (Basel, Switzerland) · 2024Article
- An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.Sensors (Basel, Switzerland) · 2024Article
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4 authors.
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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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