Lossless Compression Methods for Wireless, Real-Time Streaming of Neural Data from Neural Interface Devices

Neural interface devices generate large amounts of data that must be transmitted to a host computer for real-time neural decoding. Low-power wireless protocols, such as Bluetooth low energy (BLE), have limited data throughput, creating challenges for high-fidelity data streaming. We propose a lossless compression method designed to optimize wireless transmission without compromising neural signal integrity. Our compression pipeline includes four key stages: data filtering, cross-channel transformation, predictive coding using an autoregressive (AR) model, and Huffman coding. The method adapts to dynamic neural signal properties by recalculating AR model coefficients at regular intervals, ensuring sustained compression efficiency. This approach achieves a compression ratio between 3.02 and 4.42 while maintaining low computational and memory complexity, making it suitable for implementation on low-power microprocessors. We validated this method on a prototype neural interface using the NRF52840 system-on-chip (SoC). The device streams 16 neural data channels at a 10-kHz sampling rate to a host via a BLE 5.2 data link. The system sustains a compression ratio of 3.5 and a data throughput of 220 kb/s, facilitating real-time, lossless transmission of neural signals with minimal latency. This innovation supports the development of wireless peripheral nerve interfaces for neuroprosthetic control, offering reliable real-time data transfer from neural interfaces to neural decoders over BLE.