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

有效减少神经活动无线传输的数据 (EDnA)

• 批准号: 230027621
• 负责人: Professor Dr.-Ing. Maurits Ortmanns
• 金额: --
• 依托单位: Institut für Mikroelektronik (Mikro)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/230027621?language=en
• 关键词: Effective; data; reduction; wireless; transmission

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.

多通道信号的神经系统推导在生物技术和神经科学中具有重要意义。无论是在基础研究中，还是在假肢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体内用途：在行为科学中允许在尽可能不受干扰的环境中进行测试，以及由于更高的接受度和降低的感染风险而在人体植入中是必不可少的。尽管对这些系统的研究已经进行了十多年，但仍然存在一个未解决的主要挑战：无线传输的原始数据以10 Mbps以上的量级进行评价和使用，例如分类等。由于高数据速率，数据通信所需的功率消耗不切实际地大，这限制了当前系统在少量信道上，或者迫使它们以数据量来换取质量。该项目提出了解决这一瓶颈的挑战，通过提供数据减少而不会（显着）丢失信息：o一方面，原始数据的频谱分离允许利用-超过一个数量级-与单个神经元的尖峰相比，低频局部场电位的不同动态范围。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。此外，将采用压缩策略来减少数字数据流，几乎不丢失信息。首先，考虑增量压缩和压缩感知，因为这两种方法都受益于神经波形的相当低的活动。为了获得数据压缩质量的阈值，将使用Spikesorting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。这确保了信号不仅被定性地恢复，而且还可以被定量地恢复。o估计该方法所需的电路复杂度和功耗。这一点尤其与通过无线通信中的数据减少可以节省的功率有关。该研究项目的结果是允许多通道神经记录仪的无线原始数据传输的方法，这反过来又导致了多通道植入式神经记录仪的可能实施，具有外部可用的完整信号质量-神经科学和主动假体的工具。

项目成果

Evaluation of logarithmic vs. linear ADCs for neural signal acquisition and reconstruction

用于神经信号采集和重建的对数 ADC 与线性 ADC 的评估

• DOI: 10.1109/embc.2017.8037828
• 发表时间: 2017
• 期刊: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• 作者: M. Pagin;M. Ortmanns
• 通讯作者: M. Ortmanns

A neural data lossless compression scheme based on spatial and temporal prediction

• DOI: 10.1109/biocas.2017.8325196
• 发表时间: 2017-10
• 期刊: 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)
• 作者: Matteo Pagin;M. Ortmanns

A neural recorder IC with HV input multiplexer for voltage and current stimulation with 18V compliance

具有 HV 输入多路复用器的神经记录器 IC，用于符合 18V 电压和电流刺激

• DOI: 10.1109/esscirc.2014.6942032
• 发表时间: 2014
• 期刊: ESSCIRC 2014 - 40th European Solid State Circuits Conference (ESSCIRC)
• 作者: U. Bihr

Delta compression in time-multiplexed multichannel neural recorders

时分复用多通道神经记录器中的增量压缩

• DOI: 10.1109/prime.2016.7519487
• 发表时间: 2016
• 期刊: 2016 12th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)
• 作者: M. Pagin

Study of Compressed Sensing and Predictor Techniques for the Compression of Neural Signals under the Influence of Noise

• DOI: 10.1109/embc.2018.8512469
• 发表时间: 2018-07
• 期刊: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• 作者: Matteo Pagin;M. Ortmanns