Emotion recognition based on group phase locking value using convolutional neural network.

Electroencephalography (EEG)-based emotion recognition is an important technology for human-computer interactions. In the field of neuromarketing, emotion recognition based on group EEG can be used to analyze the emotional states of multiple users. Previous emotion recognition experiments have been based on individual EEGs; therefore, it is difficult to use them for estimating the emotional states of multiple users.

Attention allocation on mobile app interfaces when human interacts with them.

With the popularity of smartphones and the pervasion of mobile apps, people spend more and more time to interact with a diversity of apps on their smartphones, especially for young population. This raises a question: how people allocate attention to interfaces of apps during using them. To address this question, we, in this study, designed an experiment with two sessions (i.

A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features.

Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis.