Effective data reduction for wireless transmission of neural activity (EDnA)

多通道信号神经系统的派生在生物技术和神经科学中具有重要意义。无论是在基础研究方面，还是在假体控制或脑机接口方面，这些植入式系统都是一个关键的研究平台。记录仪的无线操作对于活体使用是必不可少的：在行为科学中，允许在尽可能不受干扰的环境中进行测试，以及在人体植入中，因为更高的接受度和更低的感染风险。尽管对这些系统的研究已经进行了十多年，但一个主要的未解决的挑战仍然存在：原始数据的无线传输速度超过10 Mbps，用于评估和使用，例如分类。由于高数据速率，数据通信所需的功率消耗高得不切实际，这限制了当前的系统在少数信道上，或者迫使它们在数据量和质量之间进行权衡。该项目提出了通过提供数据简化而不(显著)丢失信息的方式来解决这一挑战的解决方案：o一方面，原始数据的频谱分离允许利用与单个神经元的尖峰相反的低频局部场电位的不同动态范围--幅度超过一个数量级--的优势。频带的分离允许在频谱上限制整个记录信号的大动态范围，从而减少数据量。o此外，将采用压缩策略来减少几乎不丢失信息的数字数据流。首先考虑了增量压缩和压缩感知两种方法，因为这两种方法都得益于神经波形的低活动性。为了获得数据压缩质量的阈值，将使用Spikeorting算法，该算法允许将原始数据的分类与数据压缩和恢复后获得的分类进行比较。这不仅保证了信号的定性恢复，而且保证了信号的定量恢复。o估计了该方法所需的电路复杂性和功耗。该研究项目的结果是允许多通道神经记录器的无线原始数据传输的方法，这进而导致可能实现具有外部可用、完全信号质量的多通道植入式神经记录器--神经科学和主动假肢的工具。

Derivation of neural systems for multi-channel signals are of major importance in biotechnology and neuroscience. Both in basic research, as well as for prosthetic control or brain-machine interfaces, these implantable systems are a key research platform. A wireless operation of the recorder is essential for in vivo use: in the behavioral sciences to allow the tests in an as undisturbed environment as possible, and in human implantation due to higher acceptance and reduced risk of infection.Although research is being conducted for more than a decade on these systems, a major unsolved challenge remains: the wireless transmission of raw data in the order of more than 10Mbps for the evaluation and use, e.g. such as classification. Due to the high data rate, the required power consumption for data communication is unrealistically large, which limits current systems either on a small number of channels, or forces them to trade data quantity against quality. The project proposes solutions to this challenge to this bottleneck by means of providing data reduction without (significant) loss of information:o On the one hand a spectral separation of the raw data allows to take advantage of the - by more than an order of magnitude - different dynamic range of low-frequency local field potentials in contrast to the spikes of individual neurons. A separation of the bands allows to limit the large dynamic range of the entire recorded signal spectrally, and thereby reduce the amount of data.o In addition, compression strategies will be pursued to reduce the digital data stream almost without loss of information. At first, delta compression and compressed sensing are considered, since both methods benefit from the rather low activity of neural waveforms. In order to obtain a threshold value for the quality of the data compression, Spikesorting algorithms will be used, which allow a comparison of the classification of the raw data with that which is obtained after compression and recovery of the data. This ensures that the signals are not only recovered qualitatively, but can also be recovered quantitatively.o The required circuit complexity and power consumption of the method is estimated. This is set in particular in relation to the saved power that can be saved by a data reduction in wireless communication.The result of this research project are methods that allow wireless raw data transmission for multi-channel neuro-recorders, which in turn leads to the possible implementation of multi-channel, implantable neuro-recorders with externally available, full signal quality - a tool for neuroscience and active prostheses.