ArticleSensors (Basel, Switzerland)2021
Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Real-time brain-controlled selective hearing enhances speech perception in multi-talker environments.Nature neuroscience · 2026Article
- A Review of Auditory Attention: Neural Mechanisms, Theories, and Affective Disorders.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task.Scientific reports · 2023Article
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Authors and funding
3 authors.
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Abstract
Auditory attention detection (AAD) is the tracking of a sound source to which a listener is attending based on neural signals. Despite expectation for the applicability of AAD in real-life, most AAD research has been conducted on recorded electroencephalograms (EEGs), which is far from online implementation. In the present study, we attempted to propose an online AAD model and to implement it on a streaming EEG. The proposed model was devised by introducing a sliding window into the linear decoder model and was simulated using two datasets obtained from separate experiments to evaluate the feasibility. After simulation, the online model was constructed and evaluated based on the streaming EEG of an individual, acquired during a dichotomous listening experiment. Our model was able to detect the transient direction of a participant's attention on the order of one second during the experiment and showed up to 70% average detection accuracy. We expect that the proposed online model could be applied to develop adaptive hearing aids or neurofeedback training for auditory attention and speech perception.
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