Brain state model: A novel method to represent the rhythmicity of object-specific selective attention from magnetoencephalography data

Abstract

Object-specific selective attention can be achieved either by simultaneously splitting attention to multiple objects, or by sequentially shifting spatial attention among objects. A growing body of research show that object-specific selective attention can be implemented using the second way and that the sequential movement of attention exhibits specific rhythmicity. However, the neurocomputing mechanisms underlying this phenomenon are still not fully understood. To clarify this issue, we conducted magnetoencephalography experiments on healthy participants and subsequently proposed a computational framework based on time-series decomposition and rhythmic analysis to delve into the neural mechanisms of object-specific selective attention. Our investigation reveals that the four single-object attention states are decodable on the level of magnetoencephalography (MEG) sensor signals. Furthermore, these states manifest dynamically and rhythmically during object-specific selective attention. These findings suggest that the attentional rhythm exhibited by neural activity during object-specific selective attention is fundamentally characterized by a set of basic attentional units. This research provides valuable information for future investigations into the brain model of object-specific selective attention.