Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing

Dear Colleagues,

Bioelectric signals contain a vast amount of information, and researchers in diverse fields including cognitive neuroscience, psychiatry, and affective engineering have utilized features extracted from bioelectric signals as a reliable and objective measure of human and animal physiological activation. The introduction of easy-to-use libraries of machine learning has made various kinds of machine learning algorithms accessible to researchers outside the engineering and data-science fields. Consequently, the application of machine learning has enabled researchers to gain novel insight into physiological functions and utilize bioelectric information that has hitherto been missed or neglected by traditional methods of signal processing.

This Special Issue on “Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing” aims to provide a platform to exchange information on the state of the art of bioelectric signal processing using machine learning techniques. Researchers are invited to submit original research articles and review articles relevant to this theme. Articles on application of machine learning in adjacent areas of research such as optical imaging of neural activation, e.g., near-infrared spectroscopy, and non-contact measurement of physiological responses are also welcome. Potential topics include but are not limited to the following:

• Novel machine learning algorithm for bioelectricity data processing;
• Application of machine learning in real-time processing of bioelectric signals;
• Analysis of central and peripheral nervous system activation by machine learning;
• Automatic classification of people with/without pathological conditions;
• BCI/BMI.

Dr. Hirokazu Doi
Guest Editor

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