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The channel attention mechanism is trained to generate the mod- ulated EEG signals via an attention mask, while the CNN classifier is trained to make a detection decision. Both the channel attention mechanism and the CNN classifier are jointly trained for optimal attention decisions.
Nov 4, 2021
Abstract. Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party.
Apr 14, 2021 · Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party.
Dec 2, 2021 · In this article, we propose a neural attention mechanism that dynamically assigns differentiated weights to the subbands and the channels of EEG signals.
Apr 30, 2021 · Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech. J. Neural Eng. 14:036020. doi ...
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Apr 30, 2021 · Convolutional neural networks can decode whether a person is listening to a speaker on the left or right solely from 1 to 2s of EEG data.
This work provides a novel auditory attention detection method, and the data-driven characteristic makes it convenient for neural-steered hearing devices.
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 ...
Oct 24, 2024 · One proposed approach to help isolate an attended speech source is through decoding the electroencephalogram (EEG) and identifying the attended ...
Our results show that it is possible to decode the locus of attention within 1–2 s, with a median accuracy of around 81%. These results are promising for neuro- ...