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In this project, we build auditory attention decoding model based on human brain signal recorded by EEG. Currently we have succeeded in building a linear models with more than 80% accuracy in detecting auditory attention of human. We are going to improve the trained decoder for real-time auditory attention decoding and try to apply the neural decoder to cognitive hearing aid system and telepresence robot system.
In this project, we build auditory attention decoding model based on human brain signal recorded by EEG. Currently we have succeeded in building a linear models with more than 80% accuracy in detecting auditory attention of human. We are going to improve the trained decoder for real-time auditory attention decoding and try to apply the neural decoder to cognitive hearing aid system and telepresence robot system.
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Related Publications
J. Ha, S.C. Baek, Y. Lim, and J.H. Chung (2023) Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task. Sci Rep 13, 22682
H. An, J. Lee, M.W. Suh and Y. Lim (2023) Neural correlation of speech envelope tracking for background noise in normal hearing, Frontiers in Neuroscience, 17:1268591
J. Park, S.C. Baek, M.W. Suh, J. Choi, S.J. Kim, and Y. Lim (2023) The effect of topic familiarity and volatility of auditory scene on selective auditory attention, Hearing Research, vol. 433
S.C. Baek, J.H. Chung, and Y. Lim, (2021) Implementation of an Online Auditory Attention Detection Model with Electroencephalography in a Dichotomous Listening Experiment, Sensors, 21, 531, https://doi.org/10.3390/s21020531
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