Analysis of Neural Data
This repository includes useful MATLAB codes for ENG analysis.
There are three main stages in the algorithm: (1) spike detection, (2) spike feature selection (Wavelet), and (3) clustering of the selected spike features (K-means).
In the first step, spikes are detected with an automatic amplitude threshold on the band-pass filtered data. In the second step, a small set of wavelet coefficients from each spike is chosen as input for the clustering algorithm. Finally, the K-means classifies the spikes according to the selected set of wavelet coefficients.
- Fs = 30000 Hz; % Sampling frequency
- F_low = 300 Hz; % low pass filter for detection
- F_high =3000 Hz; % high pass filter for spike detection
Threshold:
- T_min = 5; % minimum threshold for estimated noise
- T_max = 12; % maximum threshold for avoid high amplitude artifact
Detect spike times:
- w_pre = 20; % w_pre datapoints before the spike peak are stored
- w_post =40; % w_post datapoints after the spike peak are stored
Please Run Code Main.m, the Software will run automatically and there is no need to run test code
Reza.Saadatyar@outlook.com
