Name
Abstract | ||
|---|---|---|
Spike detection in extracellular neural recordings is performed using amplitude thresholding. The threshold is set to 3 to 5 times the estimated standard deviation of the noise in the filtered recording. Robust median estimator is preferred over the conventional standard deviation estimator for the estimation to be less affected by the firing rate. There are several robust scale estimators in the literature and their employability in spike detection in extracellular neural recordings has not been studied previously. Truncation thresholds are a pair of amplitude thresholds that are used for spike detection in extracellular neural recordings. Noise standard deviation is estimated as a byproduct of the computation of the truncation thresholds. This method has been shown to yield more accurate results than the conventional standard deviation estimator and the robust median estimator. Here, the performance of four different robust scale estimators (S-n, Q(n), trimmed estimator and winsorized estimator) is compared to that of the truncation thresholds in estimating the noise standard deviation in realistic simulations of extracellular neural recordings. The results show that noise standard deviation is estimated more accurately with truncation thresholds. These findings are important for developing a suitable method for amplitude thresholding in brain-machine-interfaces. |
Year | Venue | Keywords |
|---|---|---|
2018 | Signal Processing and Communications Applications Conference | computational neuroscience,extracellular neural recording,signal detection,scale estimation |
Field | DocType | ISSN |
Truncation,Pattern recognition,Computer science,Robustness (computer science),Artificial intelligence,Thresholding,Winsorized mean,Trimmed estimator,Amplitude,Standard deviation,Estimator | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murat Okatan | 1 | 79 | 7.36 |