Brain-Machine Interfaces: Neural Systems&Cognition

4.0 credits

This upper-level course provides a comprehensive introduction to the principles and practice of brain–machine interfaces (BMIs), with emphasis on the sensory, motor, and cognitive systems that enable neural control of external devices. Students will examine the neurobiological foundations of BMI, including neural coding, population dynamics, sensorimotor integration, and the cortical mechanisms that translate intention into action. The course will cover state-of-the-art BMI technologies and the computational frameworks that support them, including neural decoding algorithms, machine learning approaches, and closed-loop control systems, as well as the translation of BMI research into clinical applications. Through critical reading of primary research articles, students will evaluate experimental design, signal processing methods, neural decoding strategies, and ethical considerations surrounding neurotechnology. The course includes guided data-analysis modules in which students explore neural decoding and closed-loop control using provided datasets and computational tools.