Brain Machine Interfaces introduces major approaches and central challenges in BMI applications. An initial overview will cover low-level details of interfacing with neural tissue, including electrode and optical designs, types of neural signals, and issues of biocompatibility and signal degradation. The core of the course will consider applications, with topics focused on (1) signal decoding approaches in motor control applications, signal to noise requirements, and effects of training and plasticity, and (2) neural stimulation, including choice of peripheral vs. central targets, consequences of topographic organization, types of perceptual responses, and limits to control of distributed systems. Special emphasis will be placed on comparing and critiquing the expanding range of applicable technologies, from in-dwelling microelectrodes to cutting edge neurophotonic tools. To follow rapid changes in the field, course materials will be drawn primarily from research literature. In addition to readings, discussion and computational exercises, students will complete a final project. This is a 4 credit course.