SSVEP-Enhanced Threat Detection and Its Impact on Image Segmentation

SSVEP-Enhanced Threat Detection and Its Impact on Image Segmentation

Shouwei Gao, Yi Cheng, Shujun Mao, Xiangyu Fan, Xingyang Deng
Copyright: © 2024 |Pages: 20
DOI: 10.4018/IJSWIS.336550
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Abstract

Selective attention, essential in discerning visual stimuli, enables the identification of threats such as snakes—a prime evolutionary influence on the human visual system. This phenomenon is encapsulated in snake detection theory (SDT), which posits that our ancestors' need to recognize these predators led to specialized perceptual abilities. This investigation utilizes steady-state visual evoked potentials (SSVEP) alongside the random image structure evolution technique, which systematically increases visual clarity through the interpolation of random noise, to probe the neural mechanisms underpinning selective attention, with a focus on serpentine forms. These findings underscore snakes' unique perceptual impact due to their curved forms and scaled textures, crucial for quick recognition—advancing image semantic segmentation and recognition tech.. This is particularly relevant for security and wildlife management, showcasing the evolutionary progression and cognitive prowess of the human visual apparatus.
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1. Introduction

Selective attention refers to the mechanism by which an individual prioritizes processing specific representations while overlooking other concurrently presented representations (Anderson, 2005). Past studies have identified different kinds of selective attention processes, often defined by the attributes of task-specific stimuli, including their location or substance. Each type of attention mechanism may involve the coordinated effort of multiple brain regions, ultimately leading to changes in sensory representation, memory, cognitive processes, or directed motor program. A prominent role of attentional processes is the effective discernment and recognition of cues associated with threats. Studies on healthy individuals indicate that cues signaling threats are identified with greater speed and precision compared to neutral ones. These mechanisms, crucial in natural environments, also play a significant role in various technological domains. For instance, they are instrumental in media-based surveillance systems, federated cloud systems, and image encryption methods that utilize quaternion Fresnel transforms (Stergiou, Psannis, Gupta, 2021; Yu et al., 2018). The ability to rapidly respond to visual threats is also evident in recent advancements in image recognition technologies that leverage visual saliency for more effective image retrieval (Wang et al., 2020). For instance, Nhi and Le explored semantic-based image retrieval, emphasizing the role of visual cognition in understanding threats (Nhi and Le., 2022; Memos et al., 2018; Mandle et al., 2022; Chopra et al., 2022). Moreover, the memories linked to these threats tend to be more resilient and less prone to disruption by unrelated information 200909). Advances in image retrieval and style transfer technologies have demonstrated the application of these cognitive principles in artificial systems as well (Qian et al., 2022), suggesting that threat cues are given processing priority, thereby affording organisms strong adaptive capabilities in their environments.

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