Andreas Keil

This study investigates attentional allocation to affective and disorder-relevant stimuli in OCD using ssVEPs and the DUC model, finding heightened distraction to unpleasant and OCD-evoking pictures in OCD participants.

This study investigates how the koniocellular visual pathway contributes to aversive learning by comparing ssVEP responses to threat and safety cues using tritan and luminance stimuli.

This paper introduces EmoFB, a deep neural network model that simulates emotional perception by integrating top-down feedback signals, showing improved recognition and alignment with human brain responses.

The paper investigates how emotional content and misophonia sensitivity affect auditory cortex responses to concurrent sounds using ASSR and other measures.

This paper introduces a biologically inspired deep neural network model for visual emotion processing and shows it accurately predicts human emotion ratings and aligns with amygdala activity.

This study shows that contingency awareness is necessary for fear conditioning, as evidenced by neural responses in the aware group during a task involving conditioned and unconditioned stimuli.

The paper compares mass univariate methods for EEG analysis using empirical data and simulations, finding cluster-based permutation tests are sensitive while permutation-based tmax is conservative.

The paper investigates rhythmic sampling of target and distractor information in a motion detection task and finds that their temporal phase relationship affects task performance.

This study compares pupil dilation and EEG alpha power during aversive conditioning, finding greater inter-individual variability in pupil dilation effects at the participant level.

This study compares emotional responses to AI-generated and naturalistic affective pictures, finding that AI pictures elicit similar self-reported and physiological responses to original stimuli.

The paper tests how distractor stimuli affect target stimulus processing by measuring decoding accuracy and behavioral performance in different brain regions.

This study compares univariate and multivariate models of emotional valence encoding in the human amygdala using fMRI, finding that core amygdala regions use global activation rather than distributed patterns.

Concept2Brain generates synthetic electrophysiological responses to text and pictures using a deep learning model, enabling reproducible EEG datasets and AI-driven brain activity modeling.

The paper investigates neural responses during non-aversive associative learning and finds increased alpha amplitude and reduced ssVEP compared to aversive conditioning.

This study shows that social anxiety disorder is linked to broader generalization of aversive responses to faces, with increased visuocortical and pupil dilation responses to facial stimuli.

This study investigates how repeated stimulus exposure affects neural responses in the primary visual cortex, revealing a two-stage learning process involving fatigue and sharpening mechanisms.

The paper validates a Bayesian multilevel learning model for estimating single-trial electrocortical responses during aversive conditioning, showing improved accuracy and handling of missing data.

The paper introduces a decoding method using temporal features from neural time series to classify experimental conditions and assess generalization across sensor locations.

The paper investigates threat processing during sustained social fear generalization using EEG and pupillometry, finding visuocortical sharpening and sustained autonomic responses without prior conditioning.

This paper introduces a method using Ricker wavelets and Bayesian bootstrap to quantify neural tuning functions in human EEG data during aversive learning, showing improved fit and interpretability compared to traditional models.

The paper investigates how auditory aversive conditioning affects alpha-band brain activity, finding threat-specific reductions and no generalization to similar tones.

This study uses steady-state visual evoked potentials to measure attentional shifts during target switching, revealing both group-level patterns and individual variability in attentional dynamics.

This review evaluates neurofeedback's impact on task performance in healthy participants, finding limited statistically significant effects and variability in study design.

The paper uses EEG-fMRI data to decode the temporal dynamics of affective scene processing, finding distinct neural activity patterns for pleasant and unpleasant images.

This study examines how aversive classical conditioning affects pupil dilation and microsaccades, finding differential responses in pupillary light response and microsaccade suppression.

This study uses computational modeling and machine learning to show that alpha-band neural oscillations are involved in updating expectations during associative learning.

This study shows that aversive conditioning sharpens visuocortical spatial tuning and reduces alpha-band activity at conditioned locations.

This study uses EEG and fMRI with machine learning to show that affective pictures evoke valence-specific neural representations in retinotopic visual cortex, influenced by limbic and frontal connectivity.

This study shows that fear conditioning leads to sparser neural representations of threat cues in primary visual cortex, associated with changes in alpha oscillations and BOLD signals.

The paper shows that threat conditioning leads to long-term visuocortical alpha suppression that resists extinction, indicating sustained attention to threat.