Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%

Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records

Preservation of private user data is of paramount importance for high Quality of Experience (QoE) and acceptability, particularly with services treating sensitive data, such as IT-based health services. Whereas anonymization techniques were shown to be prone to data re-identification, synthetic data generation has gradually replaced anonymization since it is relatively less time and resource-consuming and more robust to data leakage. Generative Adversarial Networks (GANs) have been used for generating synthetic datasets, especially GAN frameworks adhering to the differential privacy phenomena. This research compares state-of-the-art GAN-based models for synthetic data generation to generate time-series synthetic medical records of dementia patients which can be distributed without privacy concerns. Predictive modeling, autocorrelation, and distribution analysis are used to assess the Quality of Generating (QoG) of the generated data. The privacy preservation of the respective models is assessed by applying membership inference attacks to determine potential data leakage risks. Our experiments indicate the superiority of the privacy-preserving GAN (PPGAN) model over other models regarding privacy preservation while maintaining an acceptable level of QoG. The presented results can support better data protection for medical use cases in the future

Working With Environmental Noise and Noise-Cancelation: A Workload Assessment With EEG and Subjective Measures

As working and learning environments become open and flexible, people are also potentially surrounded by ambient noise, which causes an increase in mental workload. The present study uses electroencephalogram (EEG) and subjective measures to investigate if noise-canceling technologies can fade out external distractions and free up mental resources. Therefore, participants had to solve spoken arithmetic tasks that were read out via headphones in three sound environments: a quiet environment (no noise), a noisy environment (noise), and a noisy environment but with active noise-canceling headphones (noise-canceling). Our results of brain activity partially confirm an assumed lower mental load in no noise and noise-canceling compared to noise test condition. The mean P300 activation at Cz resulted in a significant differentiation between the no noise and the other two test conditions. Subjective data indicate an improved situation for the participants when using the noise-canceling technology compared to “normal” headphones but shows no significant discrimination. The present results provide a foundation for further investigations into the relationship between noise-canceling technology and mental workload. Additionally, we give recommendations for an adaptation of the test design for future studies