This is Hao Fang's personal Website

Research

My past research focuses on brain-computer interfaces (BCIs) with wide of applications below.

Area 1: Robust adaptive control algorithms for treating brain diseases. [Supported by Pilot Award]

1. Designing and Validating of a Robust and Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States. (Controller.png)
2. Robust Adaptive Deep Brain Stimulation Control of Non-Stationary Cortex-Basal Ganglia-Thalamus Network Models in Parkinson's Disease (PD.png)
3. Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder using a Brain-Computer Interface System: A Simulation Study (MDD.png)

Area 2: Toward consistent neural decoding

1. Model-Agnostic Meta-Learning Framework for EEG-Based Emotion Recognition (MAML.png)
2. SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding (SPINT.png)
3. Emotion Recognition from EEG Network Connectivity Using Low-Dimensional Discriminant Analysis on Riemannian Manifolds (Riemannian.png)

Area 3: Trajectory planning for ingelligent robots

1. Cross-Embodiment Robotic Manipulation Synthesis via Guided Demonstrations through CycleVAE and Human Behavior Transformer (Cross-embodiment.png)
2. APEX: Ambidextrous Dual-Arm Robotic Manipulation Using Collision-Free Generative Diffusion Model (Apex.png)
3. A Unified Control Framework using Diffusion Variational Autoencoder for Intercepting Flying Objects with Obstacle Avoidance (Unified.png)
4. RETRO: Reactive Trajectory Optimization for Real-Time Robot Motion Planning in Dynamic Environments (Retro.png)

Area 4: Toward future hardware integreated intracranial brain-computer interfaces

1. Toward Lightweight and Fast Inference Neural Decoder Design using Quantization Aware Training: A Simulation Study (Lightweight.png)
2. A Co-Design of Hardware and Software Systems for Future Integrated Brain-Computer Interfaces (Co-deisgn.png)