Siddharth Talwar

The paper investigates how pre- and post-stimulus neural states predict perceived audio-visual synchrony using MEG and frequency-tagging analysis.

This study uses EEG and an oddball paradigm to show the brain automatically categorizes non-verbal vocal emotions independently of acoustic features.

This study investigates the spatiotemporal neural markers of prediction error processing, specifically MMN and P300, across multisensory and unisensory modalities using EEG and MRI data from healthy humans.

The paper uses FPAS and EEG to show robust voice-selective brain responses independent of low-level acoustic features, demonstrating automatic voice categorization.

The paper links individual variability in perception to neural dynamics by analyzing EEG data and proposing a biophysical model of brain network interactions.

This paper evaluates inverse methods for EEG/MEG source localization using simulated and empirical data, finding that eLORETA better controls false positives in distributed scenarios.

This study uses EEG and computational modeling to explain inter-individual and inter-trial variability in cross-modal perception by analyzing brain connectivity patterns and neuronal interactions.