title: Impact of lossy compression on neural response characteristics extracted from high-density intra-cortical implant data.
abstract: In this paper we examine the impact of lossy wavelet compression on the information contained within high-density microelectrode array neural recordings. We have previously reported on the ability of our hardware architecture to perform under the constraints imposed by implantable hardware, as well as on its performance from a compression and signal distortion standpoint. Here we extend that work by examining the amount of information that is lost from the recorded data as a result of the finite precision integer arithmetic and thresholding operations inherent in our system. One method commonly used for the classification and sorting of recorded extracellular action potentials is principal component analysis. This technique is used to statistically obtain the most significant attributes of the spikes, thereby allowing for more accurate classification. We use the separability of the resultant clusters as a measure of the information content within the data, and present the results of simulations demonstrating the impact of various hardware design parameters on this separability.
authors.affiliation: ECE Dept at Michigan State University, East Lansing, MI 48824 USA. shetliff@egr.msu.edu
authors.forename: Michael A
authors.initials: MA
authors.lastname: Shetliffe
authors.forename: Awais M
authors.initials: AM
authors.lastname: Kamboh
authors.forename: Andrew
authors.initials: A
authors.lastname: Mason
authors.forename: Karim G
authors.initials: KG
authors.lastname: Oweiss
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D044962:Data Compression / Q000379:methods*; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity