The BCI (Brain-Computer Interface) work group focuses on advancing intra-cortical Brain-Machine Interfaces (iBMIs) by addressing key challenges such as mobility and usability. We aim to develop wireless systems to eliminate the need for wired connections, reducing infection risks and enhancing patient mobility. To overcome scalability limitations, we are exploring data compression techniques like compressive sensing (CS) and autoencoders (AE) to enable recording from up to 10,000 neurons. By integrating neuromorphic engineering, we leverage low-power, real-time processing and spiking neural networks (SNNs) that align closely with biological signals. Our projects include developing advanced control systems for prosthetics, enhancing neural decoding algorithms, and creating iBMIs for converting imagined speech to assist those with speech disorders. We also address the challenges of power dissipation and data rates in wireless iBMIs, focusing on sustainable, long-term solutions such as improved battery life and thermal management in cortical implants.