Yishu Li

131 Waterman Street

Providence, RI 02912

Hi, this is Yishu. I’m a master’s student in Biomedical Engineering at Brown University, working with the BrainGate team on intracortical Brain-computer interfaces (iBCIs) under the supervision of Dr. John Simeral. I graduated from the Biomedical Engineering department in the Southern University of Science and Technology (SUSTech) in June, 2023. I also worked as an algorithm intern at the Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences between 2023 and 2024.

I’m currently working on designing long-term stable brain-computer interface decoding algorithms for function restorations. I’m generally interested in neuroscience, deep learning, and dynamical systems. More specifically, I’m interested in developing plug-and-play (PNP) neural decoders that are robust to neural non-stationarity by combining the neuroscience knowledge (such as neural dynamics) and engineering methods (such as meta-learning, foundation models).

news

Aug 18, 2026	🏆 2026 DeeCamp – BCI Track Champion Our team took 1st place in the Brain-Computer Interface track at DeeCamp 2026, working on “Fusion Modeling of Weakly Paired EEG/fMRI Data.” We built a multi-modality EEG-fMRI foundation model to bridge high-temporal and high-spatial resolution brain signals, advancing AI-driven neuroscience research under the instruction from the ultrasound BCI company, Gestala. News link
Apr 21, 2026	I successfully defended my master’s thesis, Long-term Stable Brain-Computer Interface for Function Restoration, and received Brown University’s School of Engineering 2025 Outstanding Thesis Award (Master’s). I am deeply grateful to the BrainGate Team, especially my advisor, Dr. John Simeral, and Dr. Hadar Levi Aharoni. Award page. 🎓
Feb 01, 2026	I began my TA role for ENGN 2520: Pattern Recognition and Machine Learning at Brown in Spring 2026! 💻
Oct 01, 2024	I joined BrainGate team and started my research in intracortical Brain-computer interfaces! 🧠🦾

latest posts

Dec 01, 2025	Astrocyte Regulation Confines Representational Drift to Null-space
Aug 21, 2025	BG Deep Science: Hidden Dynamic Inference Based on Neural Manifolds
May 30, 2025	NeurANIL: A Meta-Learning Framework for Brain-to-Text Phoneme Decoding

selected publications

Characterization of Bimanual Cyclical Tasks From Single-Trial EEG-fNIRS Measurements

Yi-Chuan Jiang, Rui Ma, Shichen Qi, and 5 more authors

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022

Abs DOI

Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to-noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90° out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into low-dimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robot-assisted bimanual training.
Short-Interval Priming Effects: An EEG Study of Action Observation on Motor Imagery

Ziqi Sun, Yi-Chuan Jiang, Yishu Li, and 2 more authors

IEEE Transactions on Cognitive and Developmental Systems, Dec 2023

Abs DOI

How to effectively set and adjust training tasks is the main challenge of task-oriented rehabilitation. Recent studies have shown that motor priming effects could be used as an important reference for task setting. However, the use of assessment scales to quantify priming effects lacks timeliness. This study focused on evaluating the motor priming effects on a smaller timescale using electroencephalogram (EEG). Six healthy adults were recruited to perform motor imagery (MI) of elbow flexion extension primed by the same action observation (AO). The control task replaced AO with arrows indicating different elbows. The motor priming effects were evaluated by the event-related desynchronization (ERD) of }mu band (8–13 Hz) power and single-trial classification of left or right elbow MI. Both ERD intensity and classification accuracy of MI primed by AO were more significant than MI instructed by arrows. The results demonstrated that short-interval motor priming effects could be evaluated at the neural level and could be used as feasible neurofeedback to guide task-oriented rehabilitation.