The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities.

Mouth sounds serve several purposes, from the clinical diagnosis of diseases to emotional recognition. The following review aims to synthesize and discuss the different methods to apply, extract, analyze, and classify the acoustic features of mouth sounds. The most analyzed features were the zero-crossing rate, power/energy-based, and amplitude-based features in the time domain; and tonal-based, spectral-based, and cepstral features in the frequency domain. Regarding acoustic feature analysis, t-tests, variations of analysis of variance, and Pearson’s correlation tests were the most-used statistical tests used for feature evaluation, while the support vector machine and gaussian mixture models were the most used machine learning methods for pattern recognition. Neural networks were employed according to data availability. The main applications of mouth sound research were physical and mental condition monitoring. Nonetheless, other applications, such as communication, were included in the review. Finally, the limitations of the studies are discussed, indicating the need for standard procedures for mouth sound acquisition and analysis.