The term biosignal refers to any signal that can be measured from living organisms. Biosignals have been used in medicine, sports science, and psychology for diagnoses, and there have been impressive advancements in these areas. Recently, the fields of human-computer interaction and affective computing have found an interest in using biosignals as a means of understanding
the human state and intention. This interest has been reinforced by the fact that acquiring information with sensors and interfacing electrically with the human body have become much easier in the past few years. Moving from large analog technologies to digital ones has led to the miniaturization of sensing devices.

Wireless transmission technologies (e.g., Bluetooth low energy), which can be easily integrated with the acquisition hardware, have removed the need for bulky wiring. This talk will explore the evolution of machine learning methods for human biosignals analysis. Traditional machine
learning algorithms for feature extraction, selection, and classification will be compared with recent developments in deep learning and its applications to biosignal and general time-series
data processing.