The Mental Engagement index is an EEG-based metric designed to estimate how strongly a person is cognitively involved in a task, stimulus, or experience.

In simple terms, it reflects the extent to which the brain allocates attentional and processing resources to what an individual is doing or perceiving (Pope et al., 1995; Berka et al., 2007). 

In its most widely adopted formulation, the index is computed as the ratio between Beta activity to the sum of Alpha and Theta activity. This formulation is grounded in neuroscientific evidence showing that increased fast-frequency activity, such as Beta, is generally associated with higher cognitive activation, whereas slower rhythms, such as Alpha and Theta, are more prominent during states of reduced alertness or lower task engagement. Accordingly, higher Mental Engagement values are interpreted as indicator of stronger involvement in information processing and task execution. Several studies have shown that this index is sensitive to variations in mental workload and may also be associated with aspects of emotional experience, particularly in memory-related tasks (Pope et al., 1995; Berka et al., 2007; Chaouachi and Frasson, 2012).

Over the years, the Mental Engagement index has been widely used in both controlled laboratory settings and real-word contexts, including vigilance monitoring, learning, human-computer interaction, media evaluation, and neuroergonomics. In educational and adaptive-system research, it has proven useful for tracking the user’s cognitive participation during interaction and learning processes (Chaouachi and Frasson, 2012; Baradari et al., 2025). In applied neuroscience and neuromarketing, the index has been employed to assess how communication content and media stimuli modulate cognitive involvement over time (Cartocci et al., 2019; Vozzi et al., 2020). It has also been used in studies on working memory and stimulus modality processing, confirming its value as a compact and effective marker of task-related cognitive activation (Inguscio et al., 2021).

More broadly, recent literature in human factors and Industry 5.0 highlights Mental Engagement as a relevant neurophysiological indicator for the objective characterization of cognitive states in real-world contexts, especially when combined with other EEG and physiological measures (Ricci et al., 2025).

At BrainSigns, the Mental Engagement index is used as part of a wider set of neurophysiological measures to provide an objective and dynamic assessment of user involvement. Its sensitivity to temporal fluctuations in cognitive participation makes it particularly valuable for evaluating how people interact with interfaces, training experiences, media content, and complex operational environments.

REFERENCES

• Pope, T. T., Bogart, E. H., & Bartolome, D. S. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40(1), 187-195.
• Berka, C., Levendowski, D. J., Lumicao, M. N., Yau, A., Davis, G., Zivkovic, V. T., Olmstead, R. E., Tremoulet, P. D., & Craven, P. L. (2007). EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviation, Space, and Environmental Medicine, 78(5), B231-B244.
• Chaouachi, M., & Frasson, C. (2012). Mental workload, engagement and emotions: An exploratory study for intelligent tutoring systems. In Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science, vol. 7315. Springer.
• Cartocci, G., Modica, E., Rossi, D., Inguscio, B. M. S., Aricò, P., Martinez Levy, A. C., Mancini, M., Cherubino, P., & Babiloni, F. (2019). Antismoking campaigns’ perception and gender differences: A comparison among EEG indices. Computational Intelligence and Neuroscience, 2019, 7348795.
• Vozzi, A., Ronca, V., Rossi, D., Modica, E., Cherubino, P., Martinez, A., Giorgi, A., Inguscio, B. M. S., Babiloni, F., & Cartocci, G. (2020). Brain response to antismoking PSA, an EEG study. International Journal of Bioelectromagnetism, 22(2), 1-7.
• Inguscio, B. M. S., Cartocci, G., Sciaraffa, N., Nasta, C., Giorgi, A., Nicastri, M., Giallini, I., Greco, A., Babiloni, F., & Mancini, P. (2021). Neurophysiological verbal working memory patterns in children: Searching for a benchmark of modality differences in audio/video stimuli processing. Computational Intelligence and Neuroscience, 2021, 4158580.
• Ricci, A., Ronca, V., Capotorto, R., Giorgi, A., Vozzi, A., Germano, D., et al. (2025). Understanding the unexplored: A review on the gap in human factors characterization for Industry 5.0. Applied Sciences, 15(4), 1822.
• Baradari, D., Kosmyna, N., Petrov, O., Kaplun, R., & Maes, P. (2025). NeuroChat: A neuroadaptive AI chatbot for customizing learning experiences. In Proceedings of the 7th ACM Conference on Conversational User Interfaces, 1-21.