Hamid Karimi-Rouzbahani

This study investigates face pareidolia by analyzing EEG data during RSVP streams, showing that it results from both feedforward and top-down processes.

The paper investigates how information flow between the inferotemporal (IT) cortex and prefrontal cortex (PFC) changes with perceptual certainty, finding that feedback from PFC to IT dominates under uncertainty.

This study identifies time-resolved MEG features that generalize across individuals for decoding visual stimuli, showing early transient features outperform EEG wavelet features and suggest shared neural coding mechanisms.

This study uses fMRI and MEG to analyze information coding and exchange in the multiple demand network, revealing distinct encoding patterns and introducing Fusion-RCA for connectivity analysis.

This study introduces a decoding-based method to estimate neural time scales and finds that different visual features are encoded at distinct time scales in EEG.

This paper examines model-based and model-free representational connectivity analysis, highlighting scenarios where they give conflicting results and suggesting ways to address these issues.

The paper compares decoding visual object categories using combined vs. individual EEG features, finding that individual multiscale Wavelet coefficients outperform combined features in prediction and behavioral explanation.

This study compares the information content of EEG features with and without temporal variability, finding that variability-sensitive features improve decoding and behavioral prediction.

The paper uses MEG data to show that neural signatures of vigilance decrements can predict behavioural errors before they occur in a monitoring task.

This study shows that perceptual difficulty and familiarity level influence neural information flow direction in face recognition, with feed-forward dominance for familiar faces and top-down flow when recognition is harder.

This study evaluates the information content of EEG features during object recognition tasks, identifying ERP components as the most informative for object category processing.

This study uses EEG to track how category- and target-related information is processed in the brain during object detection, showing prefrontal areas initiate target-related processing which predicts behavioral performance better.

The paper investigates visual processing stages in peri-occipital and peri-frontal cortices using EEG and multivariate pattern analysis, revealing parallel and interactive mechanisms for category and variation information.