Advanced Machine Learning and Deep Networks for Psycho-Physiological Signals Processing, Modelling, and Classification 2021-2022
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 438
Special Issue Editor
Interests: computational models of learning and cognition; artificial neural networks and deep learning; evolutionary computing; learning technologies; bio-inspired machine learning; software engineering for AI and machine learning systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Psychophysiological signals have been demonstrated to be useful in several applications for assessing emotional experiences, modeling cognitive processes, user and player modelling, human activity recognition, classification of facial expressions, detection of behavioral changes, and so on. Signals come from a wide range of sensors, such as wearable sensors, mobile sensors, cameras, heart rate monitoring devices, EEG headcaps and headbands, ECG sensors, breathing monitors, EMG sensors, and temperature sensors. However, the use of these signals poses several challenges for reliable data processing, modeling, and classification, as it is influenced by different types of environmental and biological sources of noise, artefacts, and interference. Methods that employ machine learning and deep learning appear eminently suitable for these challenging tasks.
This Special Issue will present state-of-the-art machine learning and deep learning approaches for data processing, modeling, pattern recognition, and the classification of psychophysiological signals, and for the development of intelligent systems that use psychophysiological signals.
Prof. Dr. George Magoulas
Guest Editor
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