Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonicphasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the testu0027s questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the selfperceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1score of 75.49% for the personalised singleparticipant models and a classification F1score of 64.10% for the global models.