TY - GEN
T1 - Auditory Attention Detection with EEG Channel Attention
AU - Su, Enze
AU - Cai, Siqi
AU - Li, Peiwen
AU - Xie, Longhan
AU - Li, Haizhou
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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 across all test cases.
AB - 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 across all test cases.
UR - https://www.scopus.com/pages/publications/85122532569
U2 - 10.1109/EMBC46164.2021.9630508
DO - 10.1109/EMBC46164.2021.9630508
M3 - 会议稿件
C2 - 34892439
AN - SCOPUS:85122532569
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 5804
EP - 5807
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -