Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107481
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Cai, S | - |
dc.creator | Zhang, R | - |
dc.creator | Zhang, M | - |
dc.creator | Wu, J | - |
dc.creator | Li, H | - |
dc.date.accessioned | 2024-06-27T01:33:42Z | - |
dc.date.available | 2024-06-27T01:33:42Z | - |
dc.identifier.issn | 2379-8920 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107481 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.subject | Auditory attention | en_US |
dc.subject | Auditory system | en_US |
dc.subject | Brain modeling | en_US |
dc.subject | Convolution | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | EEG | en_US |
dc.subject | Electroencephalography | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Graph convolutional network | en_US |
dc.subject | Neurons | en_US |
dc.subject | Spiking neural network | en_US |
dc.title | EEG-based auditory attention detection with spiking graph convolutional network | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.doi | 10.1109/TCDS.2024.3376433 | - |
dcterms.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. | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | IEEE transactions on cognitive and developmental systems, Date of Publication: 12 March 2024, Early Access, https://doi.org/10.1109/TCDS.2024.3376433 | - |
dcterms.isPartOf | IEEE transactions on cognitive and developmental systems | - |
dcterms.issued | 2024 | - |
dc.identifier.scopus | 2-s2.0-85187988182 | - |
dc.identifier.eissn | 2379-8939 | - |
dc.description.validate | 202406 bcch | - |
dc.identifier.FolderNumber | a2887 | en_US |
dc.identifier.SubFormID | 48652 | en_US |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Early release | en_US |
dc.date.embargo | 0000-00-00 (to be updated) | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.