Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment
1
Center for Intelligent & Interactive Robotics, Artificial Intelligence and Robot Institute, Korea Institute of Science and Technology, Seoul 02792, Korea
2
Department of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, Seoul 04763, Korea
3
Department of HY-KIST Bio-convergence, Hanyang University, Seoul 04763, Korea
4
Research Center for Diagnosis, Treatment and Care System of Dementia, Korea Institute of Science and Technology, Seoul 02792, Korea
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(2), 531; https://doi.org/10.3390/s21020531
Received: 18 November 2020 / Revised: 7 January 2021 / Accepted: 9 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue EEG-Based Brain–Computer Interface for a Real-Life Appliance)
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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Keywords:
online auditory attention detection; electroencephalography; linear decoder model; sliding window; dichotomous listening
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MDPI and ACS Style
Baek, S.-C.; Chung, J.H.; Lim, Y. Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment. Sensors 2021, 21, 531. https://doi.org/10.3390/s21020531
AMA Style
Baek S-C, Chung JH, Lim Y. Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment. Sensors. 2021; 21(2):531. https://doi.org/10.3390/s21020531
Chicago/Turabian StyleBaek, Seung-Cheol; Chung, Jae H.; Lim, Yoonseob. 2021. "Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment" Sensors 21, no. 2: 531. https://doi.org/10.3390/s21020531
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