A novel unsupervised spike sorting implementation with variable number of features
| Autor: | Hernan G. Rey, Silvia Kochen, Fernando Julian Chaure, Quiroga R. Quian |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
business.industry Computer science Heuristic (computer science) Feature extraction Sorting Feature selection Pattern recognition Set (abstract data type) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Spike sorting Artificial intelligence Cluster analysis business Variable number 030217 neurology & neurosurgery |
| Zdroj: | 2017 XVII Workshop on Information Processing and Control (RPIC). |
| Popis: | We propose a new fully automatic spike sorting algorithm that is able to match, or even improve, the performance of semiautomatic solutions with supervised intervention from expert users. We achieved this by incorporating: 1) a set of heuristic criteria inspired by the expert actions following the solution from semiautomatic algorithms, and 2) an improved feature selection method that increases the number of units that can be isolated from a single electrode recording. We evaluated the performance of the proposed method with real and simulated data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|