Abstract
Auditory attention is a cognitive process that involves listeners to give their attention, which the listener wants to focus on. Consequently, the auditory attention processes their stimulus of interest and decodes it as a response of individuals. This study is aimed to decode the listener’s auditory attention state at continuous hearing complications in different (e.g., by quiet and hypothetical noisy background) environments. There have numerous ways to decode the auditory attention state. Here, we approach the difficulty of auditory attention to the auditory oddball paradigm based on electroencephalogram (EEG) signal. We have carried two experiments and having 840 trials for each listening state (attended and unattended). We have applied a stimulus for the attention detection model and it was verified for decoding the auditory listening attention successfully to listening. Finally, we present the decoding state of acoustic listening attention from electroencephalogram (EEG) signals through a bidirectional long short-term memory (bLSTM) and a support vector machine (SVM) model. The performance of the two models has been compared, and it reveals that these two models perform at a significant accuracy to detect the auditory listening attention state. In the frontal lobe, which is associated with attention, the SVM model has achieved a statistically significant accuracy of 74% at a 50 ms time window.
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Nasrin, F., Ahmed, N.I., Rahman, M.A. (2021). Auditory Attention State Decoding for the Quiet and Hypothetical Environment: A Comparison Between bLSTM and SVM. In: Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 1309. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_23
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DOI: https://doi.org/10.1007/978-981-33-4673-4_23
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