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DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets.

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Title: DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets.
Authors: Fan, Liangwei1 (AUTHOR), Shen, Hui1 (AUTHOR) shenhui@nudt.edu.cn, Xie, Fengyu1 (AUTHOR), Su, Jianpo1 (AUTHOR), Yu, Yang1 (AUTHOR), Hu, Dewen1 (AUTHOR)
Source: IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2022, Vol. 30, p1727-1736. 10p.
Subjects: Electroencephalography, Deep learning, Convolutional neural networks, Brain-computer interfaces, Remote-sensing images, Remote sensing
Abstract: Objective: Dim target detection in remote sensing images is a significant and challenging problem. In this work, we seek to explore event-related brain responses of dim target detection tasks and extend the brain-computer interface (BCI) systems to this task for efficiency enhancement. Methods: We develop a BCI paradigm named Asynchronous Visual Evoked Paradigm (AVEP), in which subjects are required to search the dim targets within satellite images when their scalp electroencephalography (EEG) signals are simultaneously recorded. In the paradigm, stimulus onset time and target onset time are asynchronous because subjects need enough time to confirm whether there are targets of interest in the presented serial images. We further propose a Domain adaptive and Channel-wise attention-based Time-domain Convolutional Neural Network (DC-tCNN) to solve the single-trial EEG classification problem for the AVEP task. In this model, we design a multi-scale CNN module combined with a channel-wise attention module to effectively extract event-related brain responses underlying EEG signals. Meanwhile, domain adaptation is proposed to mitigate cross-subject distribution discrepancy. Results: The results demonstrate the superior performance and better generalizability of this model in classifying the single-trial EEG data of AVEP task in contrast to typical EEG deep learning networks. Visualization analyses of spatiotemporal features also illustrate the effectiveness and interpretability of our proposed paradigm and learning model. Conclusion: The proposed paradigm and model can effectively explore ambiguous event-related brain responses on EEG-based dim target detection tasks. Significance: Our work can provide a valuable reference for BCI-based image detection of dim targets. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Neural Systems & Rehabilitation Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets.
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Neural+Systems+%26+Rehabilitation+Engineering%22">IEEE Transactions on Neural Systems & Rehabilitation Engineering</searchLink>. 2022, Vol. 30, p1727-1736. 10p.
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  Data: Objective: Dim target detection in remote sensing images is a significant and challenging problem. In this work, we seek to explore event-related brain responses of dim target detection tasks and extend the brain-computer interface (BCI) systems to this task for efficiency enhancement. Methods: We develop a BCI paradigm named Asynchronous Visual Evoked Paradigm (AVEP), in which subjects are required to search the dim targets within satellite images when their scalp electroencephalography (EEG) signals are simultaneously recorded. In the paradigm, stimulus onset time and target onset time are asynchronous because subjects need enough time to confirm whether there are targets of interest in the presented serial images. We further propose a Domain adaptive and Channel-wise attention-based Time-domain Convolutional Neural Network (DC-tCNN) to solve the single-trial EEG classification problem for the AVEP task. In this model, we design a multi-scale CNN module combined with a channel-wise attention module to effectively extract event-related brain responses underlying EEG signals. Meanwhile, domain adaptation is proposed to mitigate cross-subject distribution discrepancy. Results: The results demonstrate the superior performance and better generalizability of this model in classifying the single-trial EEG data of AVEP task in contrast to typical EEG deep learning networks. Visualization analyses of spatiotemporal features also illustrate the effectiveness and interpretability of our proposed paradigm and learning model. Conclusion: The proposed paradigm and model can effectively explore ambiguous event-related brain responses on EEG-based dim target detection tasks. Significance: Our work can provide a valuable reference for BCI-based image detection of dim targets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IEEE Transactions on Neural Systems & Rehabilitation Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1109/TNSRE.2022.3184725
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      – SubjectFull: Convolutional neural networks
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      – TitleFull: DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets.
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              Text: 2022
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