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http://hdl.handle.net/10397/107481
Title: | EEG-based auditory attention detection with spiking graph convolutional network | Authors: | Cai, S Zhang, R Zhang, M Wu, J Li, H |
Issue Date: | 2024 | Source: | IEEE transactions on cognitive and developmental systems, Date of Publication: 12 March 2024, Early Access, https://doi.org/10.1109/TCDS.2024.3376433 | Abstract: | Decoding auditory attention from brain activities, such as electroencephalography (EEG), sheds light on solving the machine cocktail party problem. However, effective representation of EEG signals remains a challenge. One of the reasons is that the current feature extraction techniques have not fully exploited the spatial information along the EEG signals. EEG signals reflect the collective dynamics of brain activities across different regions. The intricate interactions among these channels, rather than individual EEG channels alone, reflect the distinctive features of brain activities. In this study, we propose a spiking graph convolutional network, called SGCN, which captures the spatial features of multi-channel EEG in a biologically plausible manner. Comprehensive experiments were conducted on two publicly available datasets. Results demonstrate that the proposed SGCN achieves competitive auditory attention detection (AAD) performance in low-latency and low-density EEG settings. As it features low power consumption, the SGCN has the potential for practical implementation in intelligent hearing aids and other BCIs. | Keywords: | Auditory attention Auditory system Brain modeling Convolution Convolutional neural networks EEG Electroencephalography Feature extraction Graph convolutional network Neurons Spiking neural network |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on cognitive and developmental systems | ISSN: | 2379-8920 | EISSN: | 2379-8939 | DOI: | 10.1109/TCDS.2024.3376433 |
Appears in Collections: | Journal/Magazine Article |
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