Deep Learning in Biomedical Informatics and Healthcare

Dear Colleagues,

Artificial intelligence (AI) is already part of our everyday lives, and over the past few years, AI has exploded. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. Deep learning (DL) has enabled many practical applications of machine learning and by extension the overall field of AI. The concept of DL has been applied to numerous research areas, such as mental states prediction and classification, image/speech recognition, vision, and predictive healthcare. The main advantage of DL algorithms relies on providing a computational model of a large dataset by learning and representing data at multiple levels. Therefore, deep learning models are able to give intuitions to understand the complex structures of large dataset. The employment of DL algorithms in controlled settings and datasets has been widely demonstrated and recognized, but realistic healthcare contexts may present some issues and limitations, for example, the invasiveness and cost of the biomedical signals recording systems, to achieve high performance. In this regard, the aim of the Special Issue is to collect the latest DL algorithms and applications to be applied in everyday life, contexts, and various research areas, in which biomedical signals, for example, Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR), are considered and eventually combined for the user’s mental states monitoring while dealing with realistic tasks (i.e., passive BCI), the adaptation of Human–Machine Interactions (HMIs), and for an objective and comprehensive user’s wellbeing assessment such as remote and predictive healthcare applications.

Areas covered by this section include but are not limited to the following:

• Modelling and assessment of mental states and physical/psychological impairments/disorders;
• Remote and predictive healthcare;
• Transfer learning;
• Human–Machine Interactions (HMIs);
• Adaptive automation;
• Human Performance Envelope (HPE);
• Passive Brain-Computer Interaction (pBCI);
• Wearable technologies;
• Multimodality for neurophysiological assessment.

All types of manuscripts are considered, including original basic science reports, translational research, clinical studies, review articles, and methodology papers.

Dr. Gianluca Borghini
Dr. Gianluca Di Flumeri
Dr. Nicolina Sciaraffa
Assoc. Prof. Mobyen Uddin Ahmed
Ass. Prof. Manousos Klados
Guest Editors

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