In this project, efficient implementation of movement decoding for brain computer interface (BCI) will be developed by using fixed-point arithmetic. The objective of BCI is to provide a direct control pathway from brain to external devices. It is a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. This project will study the on-chip implementation of movement decoding algorithms for a low-power BCI system. In particular, the student will develop a movement decoding flow by using the HDL Coder tool offered by MATLAB Simulink. The aforementioned decoding flow will be further used to study the trade-off between power consumption and decoding accuracy for the BCI system.