نهمین کنگره نقشه برداری مغز

نمایش مقاله

Title:

Personalized Brain Mapping in Cognitive Ergonomics: Capacities and Challenges of an Individual-Centered Approach in Neuroergonomics and Human–Intelligent Systems Interaction

Subject:

Brain–Computer Interfaces (BCIs)

Presenter Author:
Name:	Mahla Nazri

Author:
Name 1:	Mahla Nazri - Corresponding - Presenter
Affiliation 1:	Mahla Nazri, PhD Student in Ergonomics, Department of Ergonomics, School of Health, Tabriz University of Medical Sciences, Tabriz, Iran.

Name 2:	Aliakbar Asil
Affiliation 2:	Ali Akbar Asil, PhD Student in Ergonomics, Department of Ergonomics, School of Health, Tabriz University of Medical Sciences, Tabriz, Iran.

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Abstract

Original Research

introduction :

Cognitive workload assessment in traditional ergonomics relies to a large extent on subjective and retrospective tools, such as the NASA-TLX questionnaire and behavioral performance indicators. These approaches, although valuable for evaluating the user's subjective experience, do not directly measure individual differences in neural and physiological patterns associated with cognitive performance and have limitations for continuous monitoring of cognitive states. With the advancement of neuroergonomics, passive brain–computer interfaces (Passive BCI), and personalized brain mapping methods, the possibility of utilizing each individual's neural features to infer cognitive states and design interactions adapted to the user's status has been enabled. The aim of this review was to investigate the capacity of personalized brain mapping in cognitive ergonomics and to elucidate the challenges of translating it into neuroadaptive systems in human–intelligent systems interaction.

Methods:

This study was conducted as a narrative review. A literature search was performed in Web of Science, PubMed, and Scopus databases for studies published from 2015 to August 2026. The primary keywords included Personalized Neuroergonomics, Passive BCI, Cognitive Workload, fNIRS, EEG, and Adaptive/Neuroadaptive Systems. Studies that addressed the utilization of neural signals to infer workload or other cognitive states, examined individual differences in neural responses, or developed adaptive and closed-loop systems were analyzed.

Results:

The reviewed evidence indicates that neural indicators associated with cognitive workload—including EEG spectral features such as theta and alpha activity and frontal cortex hemodynamic shifts in fNIRS—exhibit significant variability across individuals and, under certain conditions, within individuals. This heterogeneity can limit the generalizability of general cognitive state detection models. In several studies, the application of individual-centered calibration and modeling yielded improved classification performance compared to general models; however, the practical implementation of this approach continues to face challenges, including noise and motion/muscular artifacts, signal instability over time, individual calibration requirements, and the difficulty of generalizing models to real-world working conditions. In the event of deploying neuroadaptive systems, considerations such as user trust, system decision explainability, and neural data privacy and governance also assume fundamental importance.

Conclusion:

Personalized brain mapping helps cognitive ergonomics move away from the traditional idea of "one fixed design for all" toward intelligent systems that adapt to each user's neural characteristics and mental capacity. Applying this approach in operational work settings can serve as an effective strategy for preventing extreme fatigue, mitigating human errors, and enhancing safety in critical occupations. Nevertheless, because most existing evidence has been derived from early-stage, controlled laboratory conditions, operational translation into real-world work environments requires improving sensor precision in environments with confounding factors, streamlining setup procedures, fostering user trust, and giving serious attention to safeguarding the privacy of individuals' brain data.

Keywords:

Neuroergonomics, Personalized Brain Mapping, Cognitive Workload, Passive Brain–Computer Interface, Neuroadaptive Systems, Cognitive Ergonomics