Pipelines for analyzing EEG data and detect spindls, sleeping stages, and k-complex.
# **Code is not maintained here since 08-2018, please visit https://github.com/nmningmei/Get_Sleep_data for updated version of the data and model**
## Entering the phase using threshold detection algorithm to detect spindles in 6 EEG channels.
1. Threshold detection algorithm
2. ASI capturing alpha,beta,spindle activity in terms of power density
# [ICA parameters](martinos.org/mne/stable/auto_tutorials/plot_artifacts_correlation_ica.html):
1. lowpass at 200 Hz and highpass at 1 Hz
2. apply notch filter at 60 Hz
3. MNE ICA: iterration = 3000, fixed random state
4. artifact dectection is based on channles "LOC" and "ROC", and run automatically
5. rejection parameters: EEG: 80 - 160 depending on subjects; tstep: 2 seconds; EOG criteria: 0.4; skewness: 2; kurt: 2; variance:2
6. bandpass 0.1-50 Hz
# Detecting spindles:
1. bandpass slow/fast spindle range (10-12Hz/12-14Hz) [(Begmann et al., 2012)](http://www.ncbi.nlm.nih.gov/pubmed/22037418])
2. select channels: F3, F4, C3, C4, O1, O2
3. use a moving window to compute root-mean-square (RMS): Gaussian window, standard deviation = windown length / .68 / 2, window length = 200 samples, convolution using central part of convolution of the same size
4. compute the harmonic mean of the RMSs of the 6 channels and call it the mean channel
5. compute RMS for the mean channel
6. compute trimmed mean and trimmed standard deviation (5%) on the data after the first 100 seconds and before the last 30 seconds for both the individual channels and the mean channel
7. highpassing threshold = mean + lower_T * standard deviation (Begmann et al., 2012) and lowpassing threshold = mean + higher_T * standard deviation.
8. post threshold parameter: segments that is above the threshold and duration of the segments is in between 0.5 - 2 secs
9. determining spindles: find spindles in AT LEAST (>=) 3 channels AND find spindle in average channel at the similar time stamp (deviate < 1.0 second)
10. constructing a decision function based on spindles features, and the function determine how close a segment of the data is compared to a typical spindle. (Machine learning functions applied)
