In the realm of human-computer interaction (HCI), the fusion of artificial intelligence (AI) with brain-computer interfaces (BCIs) represents a frontier of innovation. BCIs enable direct communication between the human brain and external devices, offering unprecedented opportunities for individuals with disabilities and advancing human-machine teaming. However, the decoding of physiological signals, such as EEG, ECG, EMG etc, poses challenges due to their non-stationarity, complexity and variability. This research aims to leverage AI methodologies, such as machine learning, deep learning and signal processing, to unravel the intricate patterns within these signals. By uncovering insights into the dynamics of physiological signals, this study seeks to enhance the accuracy and efficiency of BCIs, ultimately empowering users with more intuitive and seamless interaction with technology.

The goal of this Research Topic is to bring together a collection of papers that individually and collectively used AI and signal processing approaches to solve the problems at the intersection of Human Computer Interaction, Brain Computer Interaction, Bio-medical Imaging, Neural Engineering, Neuro-rehabilitation, Neuro-informatics, Medical Robotics and Automation, Bioinformatics, Computational Medicine and Healthcare Analytics and bio-signal engineering. In doing so, these insights will produce new generation of novel, highly effective solutions for subsequent diagnostics, prognostics, predictive and detection power enhancement.

This collection welcomes the following papers: Original Research, Reviews, Systematic Reviews, Meta-analysis, Clinical Trials, Case Reports, Community Case Studies, and Study protocols.

The submission of manuscripts including, but not limited to, the following topics: Special focus will be given (but is not restricted) to:

• Human Computer Interaction, Brain Computer Interaction, Virtual Reality, Mixed Reality, Augmented Reality, Bio-medical Imaging, Neural Engineering, Neuro-rehabilitation, Neuro-informatics, Medical Robotics and Automation, Bioinformatics, Computational Medicine and Healthcare Analytics and bio-signal engineering.

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Frontiers in Medical Engineering

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