Name
Abstract | ||
|---|---|---|
Optimization of the amplitude threshold in extracellular neural recordings has recently become an active research topic in the brain-machine interface literature. In a previous study, the threshold that allows for the encoding of behavioral variables in neural activity with maximum signal-to-noise ratio has been proposed as a reasonable choice. Another good candidate, however, is the maximum likelihood estimate of the threshold. Here, these two types of threshold are estimated using extracellular recordings collected from the motor cortex (M1) of two rats performing a well-learned visuomotor task. The performance of the threshold estimates is assessed by using them in decoders. It is found that, among the four decoders examined, the method that has the best sensitivity, specificity and accuracy is logistic regression that uses the maximum likelihood estimate of the threshold. These results are important for improving the efficiency of brain-machine interfaces. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SIU.2019.8806618 | Signal Processing and Communications Applications Conference |
Keywords | Field | DocType |
computational neuroscience,spike train analysis,generalized linear models | Computational neuroscience,Pattern recognition,Computer science,Extracellular,Generalized linear model,Artificial intelligence,Motor cortex,Thresholding,Logistic regression,Amplitude,Encoding (memory) | Conference |
ISSN | Citations | PageRank |
2165-0608 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eda Dagdevir | 1 | 0 | 0.68 |
| Mehmet Kocatürk | 2 | 0 | 0.68 |
| Murat Okatan | 3 | 0 | 0.68 |