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

• Applicant: Professor Dr.-Ing. Maurits Ortmanns
• Subject Area: Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering
• Term: from 2013 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 230027621

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.

• DFG Programme: Research Grants