Bin He

Professor

Carnegie Mellon University

United States

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About

Bin He is a Professor at Carnegie Mellon University in the United States. His research focuses on neurovascular coupling, brain-computer interfaces, and the application of transcranial focused ultrasound in neurological studies. Recent contributions include work on EEG-informed fMRI analyses, deep learning-based EEG source imaging, and novel approaches to modulate cortico-thalamo-cortical pathways.

Articles (20)

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2025

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2021

Collaborators (5)

Yidan Ding

Carnegie Mellon University

UNITED STATES

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XU WANG

George Washington University

UNITED STATES

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Peter Brunner

Associate Professor

Washington University in St. Louis

UNITED STATES

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Gernot Müller-Putz

Professor

Graz University of Technology

AUSTRIA

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Gregory Worrell

Professor

Mayo Clinic Minnesota

UNITED STATES

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Bin He

Research Interests

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About

Articles (20)

Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis

EEG-based brain-computer interface enables real-time robotic hand control at individual finger level

Deep learning-based EEG source imaging is robust under varying electrode configurations

Transcranial Focused Ultrasound Modulates Feedforward and Feedback Cortico-Thalamo-Cortical Pathways by Selectively Activating Excitatory Neurons

Parameter-dependent cell-type specific effects of transcranial focused ultrasound stimulation in an awake head-fixed rodent model

Analgesic effect of simultaneously targeting multiple pain processing brain circuits in an aged humanized mouse model of chronic pain by transcranial focused ultrasound

Continuous Reaching and Grasping with a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals

Editorial: Harnessing Reviews to Advance Biomedical Engineering's New Horizons

Improving Targeting Specificity of Transcranial Focused Ultrasound in Subject Specific Head Models Using a Random Array Transducer: A k-Wave Simulation Study

Non-invasive Brain-Computer Interfaces: State of the Art and Trends

Deep Learning based Source Imaging Provides Strong Sublobar Localization of Epileptogenic Zone from MEG Interictal Spikes

Editorial A Message From the New Editor-in-Chief

Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control

Transcranial ultrasound neuromodulation induces neuronal correlation change in the rat somatosensory cortex

Progressive increase of high-frequency EEG oscillations during meditation is associated with its trait effects on heart rate and proteomics: a study on the Tibetan Buddhist

Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics

Interictal SEEG Resting‐State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome

Transcranial focused ultrasound induces sustained synaptic plasticity in rat hippocampus

On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery

Benefits of deep learning classification of continuous noninvasive brain–computer interface control

Collaborators (5)

Yidan Ding

XU WANG

Peter Brunner

Gernot Müller-Putz

Gregory Worrell

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