v0.3.0

Major release with important changes in the output of the spindles, slow-waves and REMs detection. See full changelog at https://raphaelvallat.com/yasa/build/html/changelog.html

v0.2.0

This is a major release with several new functions and enhancements. The full changelog can be found here.

v0.1.9

This is a major release with several new functions and enhancements. The full changelog can be found here.

v0.1.8

• Added yasa.plot_spectrogram() function.
• Added lspopt in the dependencies
• YASA now requires MNE>0.19.

v0.1.7

Two new functions:

• yasa.sliding_window(): calculate a sliding window of a 1D or 2D EEG signal (useful to avoid for loop when calculating epoch-by-epoch features)
• yasa.irasa(): separate the aperiodic (= fractal, or 1/f) and oscillatory component of the power spectra of EEG data using the IRASA method.

Code refactoring

• Reorganized code into several sub-files for readability (internal changes with no effect on user experience).

v0.1.6

Minor additions to the major v0.1.5 release

• Added bandpower function
• One can now directly pass a raw MNE object in several multi-channel functions of YASA, instead of manually passing data, sf, and ch_names. YASA will automatically convert MNE data from Volts to uV, and extract the sampling frequency and channel names. Examples of this can be found in the Jupyter notebooks examples.

v0.1.5

Major update

• Added REM detection (rem_detect) on LOC and ROC EOG channels + example notebook
• Added yasa/hypno.py file, with several functions to load and upsample sleep stage vector (hypnogram).
• Added yasa/spectral.py file, which includes the bandpower_from_psd function to calculate the single or multi-channel spectral power in specified bands from a pre-computed PSD (see example notebook at notebooks/10_bandpower.ipynb)

v0.1.4

Minor update

• Added get_sync_sw function to get the synchronized timings of landmarks timepoints in slow-wave sleep. This can be used in combination with seaborn.lineplot to plot an average template of the detected slow-wave, per channel.

v0.1.3

Major update

a. Added slow-waves detection for single and multi channel
b. Added include argument to select which values of hypno should be used as a mask.
c. New examples notebooks + changes in README
d. Minor improvements in performance (e.g. faster detrending)
e. Added html API (/html)
f. Travis and AppVeyor test for Python 3.5, 3.6 and 3.7

v0.1.2

Major update

• Added support for multi-channel detection via spindles_detect_multi function.
• Added support for hypnogram mask
• Added several notebook examples
• Changed some default parameters to optimize behavior