Towards Signal Dependant Data acquisition

An Adaptive 16/64 kHz, 9-bit SAR ADC withPeak-Aligned Sampling for Neural Spike Recording

Lirong Zheng , Lieuwe B. Leene , Yan Liu , Timothy G. Constandinou

Conclusion

Abstract

Peak Aligned Sampling Algorithm

Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT, UKCentre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, SW7 2AZ, UK

Email: lirong.zheng12, lieuwe.leene11, yan.liu06, t.constandinou@imperial.ac.uk

References

The recent trend towards continuous time data converters and adaptive sampling techniques have identified numerous benefits for recording biological signals over conventional converters [3]. These implementations particularly improve efficiency for recording sparse biological signals that transition between silent to active phases. This is achieved by not or coursely digitizing the recording while there is no activity and dissipating a smally amount of standby power. Similarly, by knowing the features of interest or identifying critical signal characteristics that lead to higher post-processing accuracy, resource efficiency can be improved by adapting the signal acquisition to directly look for these features.

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Figure 1: Signal processing chain in spike sorting based neural recording interfaces.

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Figure 2: Proposed concept of fine peak alignment with coarse data output. Comparison of (a) fixed uniform sampling and (b) proposed adaptive sampling method.

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Figure 5: Simulations of an Analogue recording and the adaptive sampled data.

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This work introduces a novel approach to feature-driven data acquisitionin single neuron recording systems. By dynamically adjusting the phase of the sampling clock in a Successive Approximation Register (SAR) Analogue to Digital Converter (ADC), the samples can be maximally aligned to the spike extrema (peaks). This is achieved by using spike detection to switch from a ‘coarse’ to ‘fine’ sampling clock, and triggering a peak search algorithm to determine the offset between the peak occurrence and the coarse clock. Alignment of the subsequent samples to the peak improves the temporal precision on features of interest the improve classification accuracy whilst maintaining a course effective sampling rate. As a result both quantization errors for feature comparison and digital bandwidth requirements are reduced.

Spike sorting systems allow for the decomposition of extracellular recordings and be compressed into concise single unit EAP event based data. Such a signal processing chain typically consists of analogue filtering followed by data conversion and sorting identified spike windows into clusters. Although neural interfaces maintain signal integrity by complying to the Nyquist criteria in digitization, the temporal precision of spike features can still be compromised due to quantization and aliasing errors. Specifically, spike peaks can be ‘missed’ if an incident between sampling cycles causing miss alignment or under sampled spike magnitude. The precise peak alignment is shown to be critical in template matching algorithms. Alternatively, if alignment is guaranteed template cluster dimentionality and resolution requirements can be relaxed. Effectively, this approach also reduces the digital processing and memory requirements of these methods.

The presented technique demonstrates the alternative means the recording front end can have significant impact on resource efficiency of the whole system. This all digital implementation combines high precision peak alignment with a reduced effective sampling rate for low power and bandwidth utilisation. The presented adaptive 9-bit SAR ADC achieves a FOM of 93.3 fJ/conversion-step in a standard 0.35 um CMOS technology for the adaptive 16/64 KS/s sampling rate.

[1] I. H. Stevenson and K. P. Kording, “How advances in neural recording affect data analysis,” Nature neurosience, vol. 14, no. 2, pp. 139-142, 2011.[2] D. Y. Barsakcioglu, A. Eftekah, and T. G. Constandinou, “Design optimization of front-end neural interfaces for spike sorting systems,” Proc. IEEE ISCAS, pp. 2501-2504, 2013.[3] Tsividis, Y., “Event-Driven Data Acquisition and Digital Signal Processing - A Tutorial,” Circuits and Systems II: Briefs, IEEE Transactions on, vol. 57, no. 8, pp. 577-581, 2010.

[4] J. Craninckx and G. van der Plas “A 65fJ/conversion-setp 0-to-50MS/s 0-to-0.7mW 9b charge-sharing SAR ADC in 90nm digital cmos,” Proc. IEEE ISSCC, pp. 246-250, 2007.[5] S. J. Cho, Y. Hong, T. Yoo, K. Baek, “A 10-bit, 50MS/s, 55fJ/conversion step SAR ADC with split capacitor array,” Proc. IEEE ASICON, pp. 472-475, 2011.

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AcknowledgementThis work was in part funded by EPSRC grants EP/I000569/1 and EP/K015060/1. The authors would like to thank Song Luan for useful discussion and technical support.

1) Spike Detection: A fully Programmable double sided threshold is used to detect and align to positive and negative extrema. Once a threshold crossing is detected the ADC sampling rate is increased to 64KS/s and ‘peak search’ is initiated. 2) Peak Search: The peak detection is operated though a 4 sample window in order to deal with the noise corrupted spike peak. This eliminates the influence of fluctuaions around the peak. 3) Sample Alignment: Once the peak location is confirmed, the data steam is realigned by updating the valid data phase off set of the down sampled 16KS/s stream and the system returns to the low power monitoring state.

Figure 3: State diagram of the peak alignedsampling algorithm

Figure 4: System architecture of theproposed adaptive sampling ADC topology.

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