TY - GEN
T1 - A Time-Sequence Decoding Network Based on Frequency Band Power Characteristics for Auditory Attention Decoding
AU - Zhang, Huanqing
AU - Xie, Jun
AU - Tao, Qing
AU - Ge, Zengle
AU - Xiong, Yu
AU - Li, Min
AU - Xu, Guanghua
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The human ability to locate and track a target sound source in a noisy environment, known as the 'cocktail party effect', is a well-studied phenomenon. Auditory attention decoding (AAD) using electroencephalography (EEG) has emerged as a prominent research focus in this area. Deep learning has significantly advanced AAD performance, yet traditional AAD decoders still leave room for improvement. In this paper, we enhance a widely-used end-to-end ShallowConvNet model (AAD-ShallowConvNet) to improve the detection of auditory spatial attention from EEG signals. ShallowConvNet is traditionally employed to decode EEG band power features. For this study, we optimized the parameters and modules of ShallowConvNet using AAD data. In AAD experiments utilizing a publicly available dataset, AAD-ShallowConvNet demonstrated outstanding performance across different channels and time windows. Under various evaluation conditions, AAD-ShallowConvNet outperformed other state-of-the-art algorithms.
AB - The human ability to locate and track a target sound source in a noisy environment, known as the 'cocktail party effect', is a well-studied phenomenon. Auditory attention decoding (AAD) using electroencephalography (EEG) has emerged as a prominent research focus in this area. Deep learning has significantly advanced AAD performance, yet traditional AAD decoders still leave room for improvement. In this paper, we enhance a widely-used end-to-end ShallowConvNet model (AAD-ShallowConvNet) to improve the detection of auditory spatial attention from EEG signals. ShallowConvNet is traditionally employed to decode EEG band power features. For this study, we optimized the parameters and modules of ShallowConvNet using AAD data. In AAD experiments utilizing a publicly available dataset, AAD-ShallowConvNet demonstrated outstanding performance across different channels and time windows. Under various evaluation conditions, AAD-ShallowConvNet outperformed other state-of-the-art algorithms.
KW - auditory attention decoding (AAD)
KW - brain computer interface (BCI)
KW - convolutional neural network (CNN)
KW - electroencephalography (EEG)
UR - https://www.scopus.com/pages/publications/85218631949
U2 - 10.1109/CBS61689.2024.10860576
DO - 10.1109/CBS61689.2024.10860576
M3 - 会议稿件
AN - SCOPUS:85218631949
T3 - Proceedings of the 2024 IEEE International Conference on Cyborg and Bionic Systems, CBS 2024
SP - 148
EP - 153
BT - Proceedings of the 2024 IEEE International Conference on Cyborg and Bionic Systems, CBS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Cyborg and Bionic Systems, CBS 2024
Y2 - 20 November 2024 through 22 November 2024
ER -