Methods in Neuroergonomics

The breadth of application contexts served by neuroergonomics ranges from controlled laboratory studies of cognitive load and fatigue to field deployments in aviation cockpits, clinical rehabilitation settings, and everyday consumer environments. Neuroergonomics draws on an unusually wide methodological repertoire. Researchers in the field routinely combine neuroimaging modalities, including electroencephalography, functional near-infrared spectroscopy, and functional magnetic resonance imaging with peripheral physiological measures such as electrocardiography, electrodermal activity, and electromyography, alongside eye tracking, behavioural instrumentation, and increasingly, wearable and mobile sensing platforms. This applicable breadth and methodological diversity places extraordinary demands on the methods themselves. As the field has grown in ambition and scope, so too has recognition that methodological choices at every level of the research pipeline, from hardware selection and experimental design through to signal processing, statistical analysis, and reporting, exert a profound influence on the reliability, generalisability, and practical utility of findings.Several key opportunities remain present. Therefore, we welcome submissions on the following topics:The use of innovative experimental design and analysis in service of theory, application and populations. Discussion of norms and standards for experimental design that elegantly manipulate neuroergonomic constructs, are natively linked to an ecological environment, and acknowledge the size of the population under study.• Ecological validity of neuroergonomic methods: approaches for bridging laboratory findings to field, operational, or consumer deployment contexts• Experimental design, task construction, and workload manipulation strategies tailored to neuroergonomic research questions• Statistical methods, power analysis, and approaches to handling small samples, high-dimensional data, and repeated-measures designs in neuroergonomicsThe use of complex statistical tools to accelerate scientific inquiry and understanding: novel techniques are needed to quantify complex phenomena as models, fusing multiple signal modalities (neural, physiological, behavioural, and contextual), emphasizing model transparency and interpretability while balancing individual, group, context, and temporal predictivity.• Machine learning and deep learning approaches for cognitive and affective state classification, with attention to cross-subject and cross-session generalisation• Multimodal data fusion frameworks that integrate neural, physiological, behavioural, and contextual signals• Computational and model-based approaches to quantifying cognitive load, fatigue, situational awareness, and affective state from neural and physiological data• Personalisation and individual difference modelling in neuroergonomic pipelines, including adaptive calibration and subject-independent approachesThe translation of signal acquisition and processing methods to naturalistic environments. Novel methods are needed to differentiate neuroergonomic signals from artifacts and noise in real-time to support online signal processing for closed-loop, neuroadaptive, and brain-computer interface applications.• Novel sensor hardware, electrode configurations, and wearable or mobile recording systems for use in laboratory and field neuroergonomic research• Signal processing and feature extraction methods for EEG, fNIRS, and multimodal physiological data streams in neuroergonomic contexts• Artifact detection, characterisation, and removal for neurophysiological signals acquired during movement, physical exertion, or in electrically/metabolically noisy real-world environments• Real-time and online signal processing for closed-loop, neuroadaptive, and brain-computer interface applicationsEstablishing agreed reporting standards and the availability of open, well-characterised benchmark datasets to further accelerate the cumulative development of the field.• Standardisation initiatives, shared benchmarks, and open datasets that support reproducibility and methodological convergence across the neuroergonomics community• Benchmarking and head-to-head validation of competing methods, pipelines, or devices against agreed reference standards• Open science practices in neuroergonomics: data sharing, preregistration, reproducible workflows, and standardised reportingThis Research Topic aims to bring together methodological advances that address these challenges across the full spectrum of neuroergonomic research. This collection is designed to serve as a cross-cutting methodological resource for the field. The goal is to accelerate towards robust, reproducible, and ecologically valid methods that can support both fundamental scientific research and technology deployment.Dr. Ryan McKendrick is currently employed by Northrop Grumman Systems Corporation. The other Topic Editors declare no conflicts of interest. Topic editors: Ryan McKendrick, Vítor Carvalho Source: https://www.frontiersin.org/research-topics/84049/methods-in-neuroergonomics