Human neurophysiology encompasses the intricate study of brain activity, cognition, and behavior. Recent advancements in neuroimaging and recording techniques, such as EEG, fNIRS, fMRI, and MEG, equipped with high temporal and spatial precision, have enabled non-invasive monitoring of neural dynamics. Combined with modern machine learning and pattern recognition methods, these signals can reveal subtle and complex neurophysiological patterns that serve as biomarkers of mental health, indicators of cognitive workload, or predictors of well-being. The field of Human Neurophysiology Pattern Recognition stands at the confluence of neuroscience, artificial intelligence, and biomedical engineering, pioneering innovative perspectives to discern the intricate association between brain functioning and human behavior. This burgeoning discipline promises profound advancements in fundamental neuroscience research, paving the path for translational applications across healthcare, education, and safety-critical systems.

Despite these technological advancements, the recognition and interpretation of complex patterns within human neurophysiological data remain a formidable challenge. Traditional analysis methods often struggle to encapsulate the spatiotemporal intricacies of brain dynamics or to account for individual variation when integrating multimodal data sources. These limitations hinder opportunities for the early diagnosis of neurological conditions, accurate cognitive state monitoring, and actionable healthcare applications. This Research Topic seeks to bridge these analytic gaps by gathering contributions focused on innovative machine learning approaches and multimodal fusion techniques that can reveal hidden neurophysiological patterns linked to cognitive, affective, and behavioral phenomena. Recent innovations in deep learning, explainable AI, and wearable sensing systems have empowered researchers with unprecedented modeling accuracy and clarity, paving the way for actionable insights and applications.

The scope of this Research Topic encompasses the analysis and interpretation of human neurophysiological data derived from diverse modalities, with emphasis on innovative pattern recognition and multimodal integration. We encourage articles focusing on both analytic foundations and translational frontiers, bounded by topics involving the modeling and application of neural signals in human domains. To gather further insights, we welcome articles addressing, but not limited to, the following themes:

- Signal processing and feature extraction from EEG, fNIRS, fMRI, and MEG
- Spatiotemporal and multimodal modeling of brain activity
- Application of deep learning and explainable AI to neural data
- Wearable neuroimaging devices and real-world neurophysiological assessment
- Early diagnosis, cognitive state monitoring, and neurorehabilitation techniques
- Human-computer interaction and brain–computer interface development
- Translational studies linking pattern recognition to behavioral and clinical outcomes

Topic editor Stefanos Gkikas is employed by Honda Research Institute Japan Co., Ltd.. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

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Human Neurophysiology Pattern Recognition