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

Neural responses to camouflage targets with different exposure signs based on EEG

This study investigates the relationship between various target exposure signs and brain activation patterns by analyzing the EEG signals of 35 subjects observing four types of targets: well-camouflaged, with large color differences, with shadows, and of large size. Through ERP analysis and source localization, we have established that different exposure signs elicit distinct brain activation patterns. The ERP analysis revealed a strong correlation between the latency of the P300 component and the visibility of the exposure signs. Furthermore, our source localization findings indicate that exposure signs alter the current density distribution within the cortex, with shadows causing significantly higher activation in the frontal lobe compared to other conditions. The study also uncovered a pronounced right-brain laterality in subjects during target identification. By employing an LSTM neural network, we successfully differentiated EEG signals triggered by various exposure signs, achieving a classification accuracy of up to 96.4%. These results not only suggest that analyzing the P300 latency and cortical current distribution can differentiate the degree of visibility of target exposure signs, but also demonstrate the potential of using EEG characteristics to identify key exposure signs in camouflaged targets. This provides crucial insights for developing auxiliary camouflage strategies.

EEG Techniques with Brain Activity Localization, Specifically LORETA, and Its Applicability in Monitoring Schizophrenia

Background/Objectives: Electroencephalography (EEG) is considered a standard but powerful tool for the diagnosis of neurological and psychiatric diseases. With modern imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and magnetoencephalography (MEG), source localization can be improved, especially with low-resolution brain electromagnetic tomography (LORETA). The aim of this review is to explore the variety of modern techniques with emphasis on the efficacy of LORETA in detecting brain activity patterns in schizophrenia. The study's novelty lies in the comprehensive survey of EEG methods and detailed exploration of LORETA in schizophrenia research. This evaluation aligns with clinical objectives and has been performed for the first time. Methods: The study is split into two sections. Part I examines different EEG methodologies and adjuncts to detail brain activity in deep layers in articles published between 2018 and 2023 in PubMed. Part II focuses on the role of LORETA in investigating structural and functional changes in schizophrenia in studies published between 1999 and 2024 in PubMed. Results: Combining imaging techniques and EEG provides opportunities for mapping brain activity. Using LORETA, studies of schizophrenia have identified hemispheric asymmetry, especially increased activity in the left hemisphere. Cognitive deficits were associated with decreased activity in the dorsolateral prefrontal cortex and other areas. Comparison of the first episode of schizophrenia and a chronic one may help to classify structural change as a cause or as a consequence of the disorder. Antipsychotic drugs such as olanzapine or clozapine showed a change in P300 source density and increased activity in the delta and theta bands. Conclusions: Given the relatively low spatial resolution of LORETA, the method offers benefits such as accessibility, high temporal resolution, and the ability to map depth layers, emphasizing the potential of LORETA in monitoring the progression and treatment response in schizophrenia.

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

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.