Kshitij Dwivedi

The paper investigates when mid-level visual features are processed in humans using EEG and compares this to CNNs.

The paper introduces BOLD Moments Dataset (BMD), a video fMRI dataset capturing whole-brain responses to naturalistic videos, and shows its ability to capture temporal dynamics and correlate with video memorability.

The paper shows that navigational affordances are processed later than visual features in the human brain using EEG and computational models.

The paper introduces a large EEG dataset for visual object recognition and demonstrates its utility through multiple computational modeling approaches.

This study maps brain regions to deep neural network tasks using fMRI data, showing structured correlations between tasks and visual cortex streams.

This study uses deep neural networks to analyze fMRI data and shows that scene-selective brain regions like OPA and PPA respond differently to specific scene components such as floor, wall, and ceiling.

This paper trains a deep neural network end-to-end to reconstruct images from human brain fMRI data, showing improved accuracy with more training data.

The paper shows that DNNs trained on tasks related to a brain region's function better explain its responses than those trained on unrelated tasks.