Mingjian He

The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG

The paper introduces DYNAM-O, a toolbox for analyzing sleep EEG to identify individualized transient oscillations, finding sex differences in oscillatory activity.

eyeris: A flexible, extensible, and reproducible pupillometry preprocessing framework in R

eyeris provides a reproducible pupillometry preprocessing framework that enhances FAIR principles and open science practices in pupillometry research.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

This paper introduces a dynamic generative model to extract interpretable oscillatory components from multichannel neurophysiological recordings, improving component selection and interpretation.

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

The paper identifies individualized temporal patterns as the main determinant of sleep spindle timing in humans, using data from 1008 participants.

Switching state-space modeling of neural signal dynamics

This paper develops a switching state-space model for analyzing time-varying neural data, showing improved performance in detecting transient sleep spindles from EEG recordings.

An iterative search algorithm to identify oscillatory dynamics in neurophysiological time series

This paper introduces an iterative algorithm to identify oscillatory dynamics in neural data, outperforming existing methods in accuracy and adaptability.