Brain-Computer Interfaces (BCIs) are technologies that communicate directly with the brain, and can improve the quality of life of millions of people with brain circuit disorders. Motor BCIs are among the most powerful examples of BCI technology, where microelectrode arrays are implanted into motor regions of tetraplegic participants. Movement intentions are decoded from recorded neural signals into command signals to control a computer cursor or a robotic limb. However, these systems fail to deliver the precision, speed, degrees of freedom and robustness of control enjoyed by motor-intact individuals. To enhance the overall performance of the BCI systems and to extend the lifetime of the implants, newer approaches for recovering functional information of the brain are necessary. To infer intent, BCI must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of standard techniques. In this presentation, we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all human participants, our proposed neural network led to significant offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We will show that the trained neural network can be used without modification for new datasets, brain areas and participants. We will also discuss software-hardware co-design approaches for energy-efficient hardware implementation of learning-based BCI systems towards miniaturized implantable and wearable devices.

Biography:

Azita Emami is the Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering, and the Director of Center for Sensing to Intelligence (S2I) at Caltech. She received her M.S. and Ph.D. degrees in Electrical Engineering from Stanford University in 1999 and 2004 respectively, and her B.S. degree from Sharif University of Technology in 1996. From 2004 to 2006 she was with IBM T. J. Watson Research Center before joining Caltech in 2007. She served as the Executive Officer (Department Head) for Electrical Engineering from 2018 to 2024. Her current research interests include integrated circuits and systems, integrated photonics, high-speed data communication systems, wearable and implantable devices for neural recording, neural stimulation, sensing and drug delivery.