High-fidelity compression of extracellular recordings from motor cortex

In invasive brain-machine interfaces (BMI), the recorded high-quality neural signals produce a large data volume. This calls for effective compression. In this paper, we focus on extracellular recording of motor cortex. First the characteristics of the signals are studied, one of which is that peaks of DCT coefficients at high frequency may correspond to spike firing patterns. Based on these characteristics, we propose a high-fidelity compression framework for these signals. The DCT coefficients of the signal are divided into two parts according to amplitude, rather than frequency. The Low-Amplitude-Component (LAC) is encoded by a phase called Symbol Encoding, which helps to reduce overall distortion. The High-Amplitude-Component (HAC), containing major information and spikes, is encoded by another phase called Hybrid Encoding. It combines the Huffman encoding and a novel Zero-Length-Encoding. Experiments show that the algorithm achieves a compression ratio of 18% without obvious distortion. Moreover, spikes are reserved more than 92%, outperforming existing work. Our algorithm enables low-cost storage devices to store long-time neural signals.