Christian Panitz

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

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

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