Predicting Attention Decline: An Integrated Beta-Band and SSVEP Approach for Visual Brain–Computer Interfaces

Real-time monitoring of sustained attention fluctuations during continuous complex tasks is vital for enhancing task performance and preventing accidents. Attention modulates neurons in the visual cortex in various ways to improve the visual sensitivity at an attended location. EEG-based brain-computer interfaces (BCIs) offer one of the most effective approaches for monitoring the state of human individuals. Whether transient responses evoked by brief stimuli, steady-state responses elicited by prolonged stimuli, or spontaneous neural oscillations, researchers can extract recognized electrophysiological features that reflect attention levels. However, unimodal features face inherent limitations, such as the low signal-to-noise ratio of transient responses and susceptibility of spontaneous rhythms to electrophysiological interference. Nevertheless, few studies have explored multimodal feature fusion for attention state monitoring. Here, we developed an innovative continuous go/no-go task to concurrently evoke both event-related potential (ERP) and steady-state visual evoked potential (SSVEP), while modulating spontaneous oscillatory activities through attentional engagement. To maximize the attentional modulation effect, we integrated the contrast-response functions of the modulation effect of attention on SSVEP and implemented 12 stimulus contrast levels to identify optimal visual stimulation intensity. Results from 25 subjects demonstrated that the decline in sustained attention during a continuous task was predictable before behavioral mistakes. Classification performance peaked at 31.60% stimulus contrast condition using the fused features combining spontaneous beta-band oscillations and SSVEP responses (average: 74.48%; best: 90.83%). These findings advance the development of more robust real-time attention monitoring systems based on BCI technology.