Awarded in 2020
Home Department: Electrical Engineering
Faculty Advisors: Boris Murmann (Electrical Engineering) and Tsachy Weissman (Electrical Engineering)

Research Title: Efficient Hardware Implementations for Compressive Acquisition of Neural Action Potential

Research Description: Fully-implantable intracortical brain-computer interfaces (iBCIs) have the ability to revolutionize neuroscience and medicine. However, the capability, performance, and robustness of iBCIs are limited by the current state of neural decoding algorithms. Machine Learning (ML) based algorithms outperform state-of-the-art decoders, but they require further optimization to use in fully-implantable iBCIs. Pumiao will combine neuroengineering, ML, integrated circuit design, and clinical translation to investigate a variety of machine learning/neural network algorithms, with area and power constraints in mind, in both offline and online closed-loop studies. She will then design and produce optimized ML decoder hardware to help demonstrate the potential of future fully implantable iBCIs.