Algorithms and hardware structures for efficient provision of raw data in invasive neurosystems

大量神经信号的并行采集对于医学诊断、神经假体和脑-机-接口开发等神经科学领域正在进行的研究至关重要。因此，神经植入物必须能够以准确和无损的方式以高空间和时间分辨率传递大量的神经信号。电缆驱动的脑电波侵入性测量极大地增加了严重感染的风险，这需要无线数据传输。在这方面的关键挑战是传输测量的数据量。为了无线传输以奈奎斯特速率采集的神经原始数据，未来1000个电极的电极阵列需要超过200Mbit/S的传输速率。然而，用于植入式医疗应用的最新可用的认证射频收发器提供的数据速率低于这一标准的400倍。为了缩小这一差距，并遵守完全可植入系统在能源消耗和长期组织加热方面的限制，整个信号通路的创新势在必行。解决这一目标的一种方法是在保持信号信息的同时降低采集阶段的数据速率。因此，该项目的主要目标是为创新的A/D转换器开发新的算法和硬件结构，利用采集阶段的固有信号结构来有效地减少测量数据量。为了实现这一目标，压缩感知和有限新息速率理论将被应用于神经动作电位(AP)和局部场电位(LFP)。为此，有必要发展信号理论基础并找到合适的硬件架构。从信号处理的角度来看，压缩感知和有限新息率理论将在信号建模和重构算法方面进行扩展，以利用一般信号相关性。在此基础上，将设计出对大量并行神经信号进行高效采样和重构的方法。此外，还将研究LFP和AP的同时捕获和处理。实现相应的A/D转换器首先需要确定适合高效实现的模型和算法。为了满足神经生理学的限制，必须在尽可能低的数据速率和A/D转换的能量消耗之间找到折衷方案。为了演示这种新的A/D转换和数据重建的方法，该系统将集成到现有的神经测量系统中。为了评估转换器的性能，将实现一个潜在的可植入集成电路，该集成电路采用标准CMOS制造。这样，通过压缩感知在数据压缩方面可以获得的收益可以在实验上得到证明。

The parallel acquisition of a large number of neural signals is paramount for ongoing research in the fields of neuroscience like medical diagnostics, neural prostheses and the development of Brain-Computer-Interfaces. Therefore, neural implants must be able to deliver a large number of neural signals at high spatial and temporal resolution in an accurate and lossless way. A cable-driven invasive measurement of brain waves dramatically increases the risk of serious infections, which necessitates a wireless data transmission. The key challenge in this context is the transfer of the amount of measured data. In order to wirelessly transfer neural raw data sampled at the Nyquist rate, transmission rates of over 200Mbit/s would be required for future electrode arrays with 1000+ electrodes. However, latest available certified RF transceivers for implantable medical applications offer data rates at a factor of 400 times below this. To close this gap and to comply with the restrictions of a fully implantable system in terms of energy consumption and chronic tissue heating, innovations in the entire signal path are imperative. One way to address this target is a reduction in data rate already at the acquisition stage while preserving the signals information.Therefore, the main target of this project is the development of novel algorithms and hardware architectures for innovative A/D converters exploiting inherent signal structures at the acquisition stage to efficiently reduce the amount of measured data. To achieve this target both Compressed Sensing and Finite Rate of Innovation theories will be applied to neural Action Potentials (APs) and Local Field Potentials (LFPs). To this end, it is necessary to develop the signal theory foundations and to find suitable hardware architectures.From a signal processing point of view, the theories of Compressed Sensing and Finite Rate of Innovation will be extended in terms of signal modeling and reconstruction algorithms to exploit general signal correlations. Based on this, methods for efficient sampling and reconstruction of a large number of parallel neural signals will be devised. Furthermore, the simultaneous acquisition and processing of LFPs and APs will be investigated. The implementation of according A/D converters firstly requires identification of the models and algorithms that are suitable for efficient implementation. To fulfill the neurophysiologic restrictions, a tradeoff between the lowest possible data rate and the energy consumption of A/D conversion has to be identified. In order to demonstrate this novel way of A/D conversion along with data reconstruction, the system will be integrated into an existing neural measurement system. To evaluate the performance of the converters, a potentially implantable integrated circuit fabricated in standard CMOS will be realized. This way, the achievable gains in data compression through Compressed Sensing can be proven experimentally.