Beyond Cognitive Load: AI-Based Estimation of Cognitive Effort Using Brain Signals During Digital Tasks.

Background: Cognitive effort, defined as the relationship between cognitive load and task performance, offers insight into how individuals efficiently allocate mental resources during cognitively demanding activities. This metric is crucial in high-stakes public health and clinical training, where unmanaged cognitive overload has been linked to medical errors and workforce burnout. This study aims to examine whether cognitive effort varies systematically across task segments and whether it can be estimated at the individual level using brain signal data and machine learning. Method: Functional near-infrared spectroscopy (fNIRS) data were collected from 16 participants during a structured digital cognitive task comprising four sequential segments separated by short and long rest intervals. Cognitive effort was defined through relative neural efficiency and relative neural involvement, which combined measures of prefrontal hemodynamic activity with task performance. The analysis followed a two-stage approach. First, a segment-level group analysis assessed whether cognitive effort differed significantly across predefined task segments, thereby confirming that the task structure produced meaningful changes in cognitive demand. Second, participant-independent machine learning models predicted task performance from brain signal features. These predicted performance scores were then combined with neural measures to estimate cognitive effort at the individual level. Results: First, statistical analysis showed significant differences in cognitive effort across the four task segments. This confirms that even small changes in the assessment structure impact on the collective cognitive efficiency of the trainees. Then, we used machine learning on fNIRS data to predict individual performance scores. We found that the effort calculated from these predicted scores was nearly identical to that calculated from the actual scores. This result suggests that our effort metric is strongly based on brain signals. Conclusion: The findings demonstrate the feasibility of estimating cognitive effort from brain signals using artificial intelligence at both group and individual levels. Public Health Implications: Estimation of cognitive effort from digital task data may support scalable monitoring of cognitive workload and mental fatigue in technology-mediated environments, which complements subjective assessment methods used in public and digital health research.