Name
Abstract | ||
|---|---|---|
Spikes detection is very important to the neural information study. However, the original neural signals collected by the microelectrode contain a lot of noise. Sometimes, spikes detection is hard to achieve when SNR (Signal to Noise Rate) is very low. At present, the fractal theory has been widely applied, and fractal dimension is very sensitive to fluctuation of curves. The fractal theory is introduced to the preprocessing of neural signals in this paper. It detected spikes by calculating fractal dimension of the original data. Experiments show that, fractal dimension can sign fluctuation of curve. This method can effectively detect the low amplitude spikes in the noise. The effect of spike detection based on fractal dimension is better than the usual threshold method and energy method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GreenCom-iThings-CPSCom.2013.340 | GreenCom/iThings/CPScom |
Keywords | Field | DocType |
fractals,neurophysiology | Neurophysiology,Pattern recognition,Fractal dimension,Signal-to-noise ratio,Fractal,Speech recognition,Preprocessor,Artificial intelligence,Energy method,Amplitude,Microelectrode,Physics | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji-Yang Zhou | 1 | 0 | 0.68 |
| Sheng-Wei Xu | 2 | 14 | 7.43 |
| Nansen Lin | 3 | 0 | 2.37 |
| Mixia Wang | 4 | 1 | 4.10 |
| Xinxia Cai | 5 | 1 | 9.85 |