Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings

PLOS ONE, Dec 2019

Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.

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Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings

Johansson AJ (2014) Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings. PLoS ONE 9(4): e93779. doi:10.1371/journal.pone.0093779 Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings Palmi Thor Thorbergsson 0 Martin Garwicz 0 Jens Schouenborg 0 Anders J. Johansson 0 Maurice J. Chacron, McGill University, Canada 0 1 Department of Experimental Medical Science, Lund University, Lund, Sweden, 2 Department of Electrical and Information Technology, Lund University, Lund, Sweden, 3 Neuronano Research Center, Lund University , Lund , Sweden Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used. - Funding: This work was supported by a Linnaeus Grant from the Swedish Research Council (no. 60012701), a grant from the Knut and Alice Wallenberg Foundation (no. 2004.0119) and the Medical and Engineering Faculties at Lund University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Brain-machine interfaces (BMIs) are systems that provide a signal pathway between the central nervous system (CNS) and the outside world and thereby a means of observing the neuronal activity that underlies behavior. One class of BMIs employ intracranially implanted microelectrodes to record the changes in extracellular potential induced by activities of neurons surrounding them [1]. The extracellular recording is composed of spikes representing action potentials in near-by neurons, noise consisting of spikes from distant neurons, local field potentials (LFPs) representing synaptic activity and thermal noise generated in the recording electronics [2]. By isolating the spiking components of the individual neurons that contribute to the extracellular recording, the firing patterns of those neurons can be characterized and correlated with events or learning processes in the motor or sensory domains [3] to reveal the dynamics of neuronal circuits that govern behavior. The fundamental steps in this procedure are (a) spike detection and extraction, (b) spike alignment and (c) spike sorting. Spike detection is commonly based on detecting the local increase in signal energy or amplitude associated with the firing of a spike and its aim is to pinpoint the temporal occurrence of spike waveforms. Spike alignment is important for the subsequent spike sorting step and it involves shifting the detected spike waveforms in time to have them aligned with respect to a given waveform landmark, e.g. the point of maximum amplitude. Spike sorting typically involves first extracting spike features (i.e. waveform characteristics that ideally are the same for spikes coming from the same neuron but different for spikes coming from different neurons) and then classifying the spikes based on the extracted features [4]. BMIs are often implemented with multiple recording channels (multielectrode arrays), which results (...truncated)


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Palmi Thor Thorbergsson, Martin Garwicz, Jens Schouenborg, Anders J. Johansson. Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings, PLOS ONE, 2014, Volume 9, Issue 4, DOI: 10.1371/journal.pone.0093779