GAUDIE: Development, validation, and exploration of a naturalistic German AUDItory Emotional database

Since thoroughly validated naturalistic affective German speech stimulus databases are rare, we present here a novel validated database of speech sequences assembled with the purpose of emotion induction. The database comprises 37 audio speech sequences with a total duration of 92 minutes for the induction of positive, neutral, and negative emotion: comedian shows intending to elicit humorous and amusing feelings, weather forecasts, and arguments between couples and relatives from movies or television series. Multiple continuous and discrete ratings are used to validate the database to capture the time course and variabilities of valence and arousal. We analyse and quantify how well the audio sequences fulfil quality criteria of differentiation, salience/strength, and generalizability across participants. Hence, we provide a validated speech database of naturalistic scenarios suitable to investigate emotion processing and its time course with German-speaking participants. Information on using the stimulus database for research purposes can be found at the OSF project repository GAUDIE: https://osf.io/xyr6j/

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available

GAUDIE: Development, Validation and Exploration of a Naturalistic German AUDItory Emotional Database

Since thoroughly validated naturalistic affective German speech stimulus database are rare, we present here a novel validated database of speech sequences assembled with the purpose of emotion induction. The database comprises 37 audio speech sequences for the induction of positive, neutral, and negative emotion: comedian shows intending to elicit humorous and amusing feelings, weather forecast, and arguments between couples and relatives from movies or television series. Multiple continuous and discrete ratings are used to validate the database to capture the time course and variabilities of valence and arousal. We analyse and quantify how well the audio sequences fulfil quality criteria of differentiation, salience/strength, as well as generalizability across participants. Hence, we provide a validated speech database of naturalistic scenarios suitable to investigate emotion processing and its time course with German speaking participants

Classifying Mental Effort in a Quasi-Realistic Scenario Based on Multimodal Data Fusion

Cross-subject multilevel classification approach of mental effort based on neurophysiological data

Brain oscillation entrainment by perceptible and non-perceptible rhythmic light stimulation

Keywords: Neuroergonomics, SSVEPs, Non-perceptible stimulation, Attention, Entrainment of oscillations, Functional connectivity, Vehicle interior Detailed description is provided by publications after a completed reviewer process

Examining joy of use and usability during mobile phone interactions

Examining joy of use and usability during mobile phone interactions within a multimodal methods approac

Oscillatory EEG Signatures of Affective Processes during Interaction with Adaptive Computer Systems

Affect monitoring is being discussed as a novel strategy to make adaptive systems more user-oriented. Basic knowledge about oscillatory processes and functional connectivity underlying affect during naturalistic human–computer interactions (HCI) is, however, scarce. This study assessed local oscillatory power entrainment and distributed functional connectivity in a close-to-naturalistic HCI-paradigm. Sixteen participants interacted with a simulated assistance system which deliberately evoked positive (supporting goal-achievement) and negative (impeding goal-achievement) affective reactions. Electroencephalography (EEG) was used to examine the reactivity of the cortical system during the interaction by studying both event-related (de-)synchronization (ERD/ERS) and event-related functional coupling of cortical networks towards system-initiated assistance. Significantly higher α-band and β-band ERD in centro-parietal and parieto-occipital regions and β-band ERD in bi-lateral fronto-central regions were observed during impeding system behavior. Supportive system behavior activated significantly higher γ-band ERS in bi-hemispheric parietal-occipital regions. This was accompanied by functional coupling of remote β-band and γ-band activity in the medial frontal, left fronto-central and parietal regions, respectively. Our findings identify oscillatory signatures of positive and negative affective processes as reactions to system-initiated assistance. The findings contribute to the development of EEG-based neuroadaptive assistance loops by suggesting a non-obtrusive method for monitoring affect in HCI

MindTrain. How to Train Your Mind with Interactive Technologies

Technological products for training the mind that support subjective well-being are gaining popularity in our daily lives. Using Electroencephalographic (EEG) signals for neurofeedback is helpful for learning and a promising approach to train the mind. We introduce MindTrain, a novel, gamified neurofeedback training environment that allows users to learn the skill to voluntarily self-regulate their brain activity in Virtual Reality (VR). MindTrain combines the concept of implicit control with a mobile consumer EEG-wearable in an interactive and immersive VR-environment for visualising the feedback. We tested the feasibility of MindTrain for training to control states of relaxation and concentration. Our results prove that MindTrain is a promising novel method that warrants further investigation within a larger study. Furthermore, the use of the mobile EEG-wearable demonstrates the potential for bringing MindTrain out of the laboratory into a real-world context