Neural mechanism for dynamic distractor processing during video target detection: Insights from time-varying networks in the cerebral cortex

Song, Xiyu; Zeng, Ying; Li, Tong; Shu, Jun; Li, Huimin; Yan, Bin

doi:10.1016/j.brainres.2021.147502

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…The brain response intensity gradually increases and then weakens; correspondingly, the brain network connection gradually enriches and then weakens. Therefore, it is necessary to construct an adjacency matrix according to the continuous change of the brain connection network ( Desmedt, 1980 ; Wang et al, 2014 ; Song et al, 2021b ).…”

Section: Methodsmentioning

confidence: 99%

“…At present, the graph connection representation in EEG data based on graph convolution only uses a static connection mode, which is not consistent with the fact that the brain network connection changes from time to time. Especially, for video-induced P3, the neural response can be divided into three stages, i.e., information integration, decision process, and neuronal response ( Desmedt, 1980 ; Song et al, 2021b ). Therefore, this paper proposes a segmentation adaptive spatial-temporal GCN (SAST-GCN) for single-trial video-induced P3 detection based on the spatial-temporal structure of multi-channel EEG signals.…”

Section: Introductionmentioning

confidence: 99%

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SAST-GCN: Segmentation Adaptive Spatial Temporal-Graph Convolutional Network for P3-Based Video Target Detection

Zeng

Zhang

et al. 2022

Front. Neurosci.

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Detecting video-induced P3 is crucial to building the video target detection system based on the brain-computer interface. However, studies have shown that the brain response patterns corresponding to video-induced P3 are dynamic and determined by the interaction of multiple brain regions. This paper proposes a segmentation adaptive spatial-temporal graph convolutional network (SAST-GCN) for P3-based video target detection. To make full use of the dynamic characteristics of the P3 signal data, the data is segmented according to the processing stages of the video-induced P3, and the brain network connections are constructed correspondingly. Then, the spatial-temporal feature of EEG data is extracted by adaptive spatial-temporal graph convolution to discriminate the target and non-target in the video. Especially, a style-based recalibration module is added to select feature maps with higher contributions and increase the feature extraction ability of the network. The experimental results demonstrate the superiority of our proposed model over the baseline methods. Also, the ablation experiments indicate that the segmentation of data to construct the brain connection can effectively improve the recognition performance by reflecting the dynamic connection relationship between EEG channels more accurately.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%