Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays
Fig 6
Population results from thirteen retinal preparations reveal the efficacy of the algorithm.
A. Trial-by-trial wise performance of estimators broken down by the the four types of stimulation considered (total number of trials 1,713,233, see Table 1 S1 Text for details). B. Trial-by-trial wise performance of estimators to perturbations of real data (only single-electrode): five trials per stimulus for trial subsampling, every other stimulus for amplitude subsampling and σ = 20 for noise injection. C,D. Amplitude-series wise performance of estimators. C: false omission rate (FOR = FN/(FN+TP)), false discovery rate (FDR = FP/(FP+TP)), and error rate based on the 4,045 available amplitude series (see Table 2 S1 Text for details); D: comparison of activation thresholds (human vs. kernel-based algorithm). E. Performance measures (trial-by-trial) broken down by distance between neuron and stimulating electrode. F. Trial-by-trial error as a function of EI peak strength across all electrodes (only kernel-based). A Spearman correlation test revealed a significant negative correlation. G. Error as a function of number of iterations in the algorithm. H. For the true positives, histogram of the differences of latencies between human and algorithm. I. Computational cost comparison of the three methods for the analysis of single-electrode scans, with 20 to 25 (left) or 50 (right) trials per stimulus.