Kabir Arora

The paper introduces a method to track covert attention using phase-modulated RIFT-EEG with multiple tags, enabling fine-resolution spatial and temporal attention mapping.

The paper shows that anticipating stimulus relevance enhances visual processing to support memory-guided selection.

The paper investigates whether visual attention travels or teleports during covert shifts, finding no evidence of intermediate processing enhancement.

The paper investigates how bottom-up saliency and top-down goals compete in early visual cortex using EEG and RIFT, showing dynamic suppression of distractors over time.

The paper measures proactive and reactive attentional markers in an EEG-eyetracking task and shows they are independent processes, with gaze bias predicting behavior.

This study demonstrates that covert attention can be reliably measured using RIFT on a 360Hz consumer monitor, expanding accessibility for cognitive neuroscience research.

The paper investigates the neural mechanisms of external and internal attentional selection using EEG and RIFT, finding that only external attention enhances sensory processing.

The paper re-analyzes an experiment showing that gaze biases during a memory task reflect attention to task-relevant stimulus endpoints rather than memorized orientations.

This paper implements and validates the first RIFT-EEG setup, showing that RIFT can evoke measurable oscillatory responses in EEG comparable to MEG.

This paper proposes a heuristic model of motor control that integrates movement from target to endpoint, simulates human arm motion, and addresses degrees of freedom in robotics.

LayNii is a software suite designed for analyzing layer-specific functional MRI data with SNR and coverage constraints, offering tools for layerification, smoothing, and artifact mitigation.