DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems

This work presents an area and power efficient encoding system for wireless implantable devices capable of monitoring the electrical activity of the brain. Such devices are becoming an important tool for understanding, real-time monitoring, and potentially treating mental diseases such as epilepsy and depression. Recent advances on compressive sensing (CS) have shown a huge potential for sub-Nyquist sampling of neuronal signals. However, its implementation is still facing critical issues in delivering sufficient performance and in hardware complexity. In this work, we explore the tradeoffs between area and power requirements applying a novel DCT Learning-Based Compressive Subsampling approach on a human iEEG dataset. The proposed method achieves compression rates up to 64x, increasing the reconstruction performance and reducing the wireless transmission costs with respect to recent state-of-art. This new fully digital architecture handles the data compression of each individual neural acquisition channel with an area of 490 x 650/μm in 0.18 μm CMOS technology, and a power dissipation of only 2μW.