Abstract
Short-time auditory attention detection (AAD) based on electroencephalography (EEG) can be utilized to help hearing-impaired people improve their perception abilities in multi-speaker environments. However, the large individual differences and very low signal-to-noise ratio (SNR) of EEG signals may prevent the AAD from working effectively across subjects in a short time duration. To address the above issues, this paper firstly used a sparse autoencoder with the same trial constraint (SAE-T) method to extract common features across subjects from EEG signals in a 2-s time window. Then we use a CNN-based speech temporal amplitude envelopes (TAEs) reconstruction model for attention detection by comparing the reconstructed accuracy of attended with unattended speech, and the time delay and segmented SAE-T features were also considered in the model. Moreover, the dataset we used has no directional information of speech, which can train a more general model for practical application. Experimental results show that the proposed method can achieve AAD detection accuracy to 86.31%, higher than the method of removing time delay or segmented SAE-T features.
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This work was supported by the National Natural Science Foundation of China (No. 61876126 and 61503278).
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Yang, K., Zhang, Z., Zhang, G., Masashi, U., Dang, J., Wang, L. (2023). An Improved Stimulus Reconstruction Method for EEG-Based Short-Time Auditory Attention Detection. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_23
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