EEG-based cognitive load estimation during the use of a virtual wheelchair simulator

Introduction Driving a powered wheelchair is a complex task that requires the integration of motor, visual, and cognitive skills. The development of assistive technologies without appropriate assessment methods that help bridge the gap between users and developers may lead to abandonment and reduced engagement. Most assessments rely on explicit measures, such as performance metrics, or subjective tools like interviews and questionnaires. In contrast, implicit measures allow continuous inference of mental states during task execution. This study proposes the use of blink indices derived from electroencephalographic (EEG) signals as implicit metrics to estimate cognitive load during the use of a virtual reality wheelchair training simulator. Methods A total of 25 participants (14 females and 11 males; mean age 26.50 ± 5.7 years) completed a predefined route using a virtual wheelchair simulator. Blink parameters, including frequency, duration, and velocity, were extracted from EEG signals during task performance. After completing the simulation, participants responded to the NASA Task Load Index (NASA-TLX) to assess subjective cognitive load, as well as the System Usability Scale (SUS) and the Igroup Presence Questionnaire (IPQ). Results The findings showed that higher mental-visual demand was associated with decreases in blink frequency, duration, and velocity. Correlation analyses between NASA-TLX scores and blink parameters revealed weak to moderate associations. These results suggest partial convergence between subjective and physiological measures of cognitive load. Discussion Blink-based indices derived from EEG signals provide relevant information regarding cognitive demand during wheelchair simulator use. However, blink parameters alone are insufficient to reliably infer cognitive load. When combined with subjective questionnaires, implicit physiological metrics may offer a more comprehensive assessment than questionnaires alone, supporting the development and refinement of assistive training technologies.