Steady-state visual evoked potential and its applications in studying visual selective attention

Chen, Xiaoyu; Cheng, Zi-Jian; Hu, Chengmi; Liang, Tengfei; Liu, Qiang · 2020 · OpenAlex-citations

DOI: 10.1360/tb-2020-0009

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Summary

This review paper addresses the application of steady-state visual evoked potentials (SSVEP) as a physiological metric for studying visual selective attention. The authors were motivated by the need to fill a gap in Chinese literature, as existing reviews focused primarily on brain-computer interfaces (BCI) rather than cognitive applications, and previous comprehensive reviews lacked specificity regarding attention research. The paper aims to clarify why SSVEP is a superior indicator for attention allocation compared to traditional event-related potentials (ERP) and to summarize its mechanisms, methodologies, and current applications. The authors analyze the electrophysiological mechanisms of SSVEP, discussing the "phase reset" and "superposition" theories, and identifying the primary visual cortex (V1) as the main neural source. The core methodological approach reviewed is "frequency tagging," where stimuli are presented at specific periodic frequencies to induce corresponding SSVEP responses. The amplitude of these responses serves as an index of attentional resource allocation. The paper distinguishes between two tagging strategies: spatial embedding, where different spatial locations flicker at different frequencies, and temporal embedding, such as fast periodic visual stimulation (FPVS), where different items are presented sequentially at the same location. The review synthesizes findings from studies utilizing these paradigms to investigate spatial attention, feature-based attention, and object-based attention. Key findings indicate that SSVEP amplitude generally correlates positively with attentional resources, though this relationship can be negative in the alpha frequency band (8–13 Hz). The paper highlights SSVEP’s advantages over ERP: it allows for continuous monitoring of attentional dynamics rather than transient sampling, imposes fewer constraints on stimulus spatial arrangement, and enables the simultaneous analysis of multiple stimuli without the confounding effects of hemispheric differences required in ERP studies. Specific applications reviewed include using SSVEP to map the flexibility of spatial attention, explore inhibition of return, and investigate feature-based selection using random dot kinematograms. The authors note that while SSVEP is effective for spatial and feature-based attention, it faces challenges in studying object-based attention due to difficulties in frequency-tagging overlapping objects without introducing frequency as a distinguishing feature. The significance of this work lies in establishing SSVEP as a robust, high-resolution tool for cognitive neuroscience. The authors conclude that future research should integrate SSVEP with variables such as emotion, reward, and working memory to deepen the understanding of attentional mechanisms. Additionally, they propose that algorithms developed for SSVEP-based BCIs could be adapted to simplify experimental designs in attention research by decoding attentional states from simultaneous multi-frequency stimuli, thereby reducing trial counts and participant burden.

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• attention selective divided