We are pleased to announce this Volume II Research Topic which aims to build upon the success, and interesting discussions raised within the initial collection in this series - 'Advancements in Smart Diagnostics for Understanding Neurological Behaviors and Biosensing Applications'.

The intersection of machine learning, biosensing technologies, and neurological behavior analysis presents fertile ground for groundbreaking research and innovation. This research topic aims to bridge the gap between these two domains, focusing on leveraging machine learning techniques to gain deeper insights into neurological behaviors while advancing smart biosensing applications for enhanced healthcare and human-computer interaction.

Recent strides in machine learning, deep learning, and explainable AI have opened doors to unprecedented opportunities in understanding the complexity of neurological behaviors. From deciphering intricate patterns to facilitating early diagnosis and personalized treatment, these technologies offer immense potential in neurology and cognitive sciences.

Simultaneously, the evolution of biosensing methods such as EEG, EMG, ECG, fNIRS, and others has revolutionized physiological monitoring and detection of human neuroscience behaviors, paving the way for intelligent healthcare solutions and human-computer interfaces. However, integrating machine learning with biosensing data poses unique challenges, including explainable AI models, neurobehavior modeling, data privacy, and ethical considerations.

This Research Topic invites contributions from researchers across academia, industry, and healthcare sectors to explore novel approaches at the convergence of machine learning and biosensing for understanding neurological disorders, human neuroscience behaviors and advancing smart healthcare applications. Topics of interest include, but are not limited to:

- Development of machine learning models for neurological behavior analysis using biosensing data
- Integration of multiple physiological modalities for real-time monitoring and diagnosis
- Explainable AI techniques for interpreting neurological behaviors and enhancing model transparency
- Ethical considerations and privacy-preserving techniques in machine learning applications for healthcare
- Implementation of IoT infrastructure and cybersecurity measures to ensure data integrity and confidentiality
- Utilization of encryption methods to safeguard sensitive biosensing data transmitted over IoT networks
- Case studies showcasing practical implementations and success stories in smart biosensing and neurological behavior analysis

Join us in this endeavor to unlock new frontiers in neuroinformatics, neuroscience, and smart healthcare innovation and pave the way for a smarter, more personalized approach to understanding and managing neurological conditions. We welcome authors to submit original research articles, reviews, method articles, and case studies that contribute to the depth and breadth of this innovative and exciting field.

Don't miss the chance to make your manuscript contribution and showcase your research alongside the work of other outstanding colleagues. Register your interest in the link below so our Topic Editors can make sure to extend deadlines for your submission if needed: Participate in this topic

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Advancements in Smart Diagnostics for Understanding Neurological Behaviors and Biosensing Applications - Volume II