Neuroscience

Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.

This dataset contains information about published papers on how biological signals (ECG, EEG, EDA and MG + eye-tracking) are being used and collected in the field of video games. This dataset reflects the information published including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the main results reported from their use.

The use of modern Mobile Brain-Body imaging techniques, combined with hyperscanning (simultaneous and synchronous recording of brain activity of multiple participants) has allowed researchers to explore a broad range of different types of social interactions from the neuroengineering perspective. In specific, this approach allows to study such type of interactions under an ecologically valid approach.

The EegDot data set collected using a Cerebus neural signal acquisition equipment involed thirteen odor stimulating materials, five of which (smelling like rose (A), caramel (B), rotten (C), canned peach (D), and excrement (E)) were selected from the T&T olfactometer (from the Daiichi Yakuhin Sangyo Co., Ltd., Japan) and the remaining eight from essential oils (i.e., mint (F), tea tree (G), coffee (H), rosemary (I), jasmine (J), lemon (K), vanilla (L) and lavender (M)).