Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces

Motor Imagery (MI) Brain-Computer Interfaces (BCIs) represent a promising technology for neurorehabilitation and assistive control. However, the clinical viability of these systems is frequently hindered by the inherent limitations of electroencephalography (EEG) with regard to its low signal-to-noise ratio (SNR), non-stationarity, and high inter-subject variability. Standard decoding methods often fail to capture the complexity of user intention leading to unreliable performance and user frustration. This review proposes a solution to these challenges by advocating for the integration of passive eye movements (EM) as a complementary data stream. The theoretical rationale for this approach rests on the neurocognitive principle of functional equivalence. Because imagined actions recruit similar visuomotor networks to those used in physical execution, EM constitute a robust correlate of the underlying neural simulation. We distinguish this approach from conventional hybrid systems that use gaze coordinates for active control. Instead, we argue for a framework of passive monitoring where oculomotor metrics, including pupil diameter, fixation patterns, and saccadic dynamics, serve as a continuous window into the user's cognitive state. We synthesize evidence demonstrating that these passive signals can reliably index cognitive load, attentional allocation, and covert motor planning. By fusing these behavioral metrics with EEG, a BCI can disambiguate uncertain neural patterns and verify user intent without imposing additional task demands. Furthermore, we discuss how this multimodal integration enables the development of adaptive classifiers that respond to fluctuations in user fatigue and engagement. Bridging the gap between cognition and control through passive EM monitoring offers a pathway to create BCI systems that are intrinsically responsive to the user's internal state.