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bioRxiv · 10.1101/2021.04.13.439732

Auditory attention detection with EEG channel attention

Cai, S.·Su, E.·Li, P.·Xie, L.·Li, H.
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Abstract

Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 88.3% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin in all test cases.

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Cai, S., Su, E., Li, P., Xie, L., Li, H.. 2021-04-14. Auditory attention detection with EEG channel attention. https://doi.org/10.1101/2021.04.13.439732

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