nbara · nbara · Oct 20, 2020 · Oct 20, 2020 · Oct 20, 2020 · Oct 20, 2020
diff --git a/.travis.yml b/.travis.yml
@@ -4,7 +4,7 @@ branches:
 notifications:
   email: false
 language: python
-sudo: required
+os: linux
 dist: xenial
 python:
   # We don't actually use the Travis Python, but this keeps it organized.
@@ -24,10 +24,10 @@ script:
   - make html
   - touch _build/html/.nojekyll
 deploy:
-  provider: pages:git
+  provider: pages
   token: $GITHUB_TOKEN
   edge: true # opt in to dpl v2
-  skip_cleanup: true
+  cleanup: false
   keep_history: false
   local_dir: _build/html
   target_branch: gh-pages

diff --git a/README.md b/README.md
@@ -31,13 +31,13 @@ pip install .
 
 *Note* : Use developer mode with the `-e` flag (`pip install -e .`) to be able to modify the sources even after install.
 
-## Python implementation of CCA, STAR, SNS, DSS, and robust detrending
+## References
 
-This is mostly a translation of Matlab code from the [NoiseTools toolbox](http://audition.ens.fr/adc/NoiseTools/) by Alain de Cheveigné. It builds on an initial python implementation by [Pedro Alcocer](https://github.com/pealco).
+### 1. CCA, STAR, SNS, DSS, ZapLine, and robust detrending
 
-Only CCA, SNS, DSS, STAR and robust detrending have been properly tested so far. ZapLine and TSCPA may give inaccurate results due to insufficient testing (contributions welcome!)
+This is mostly a translation of Matlab code from the [NoiseTools toolbox](http://audition.ens.fr/adc/NoiseTools/) by Alain de Cheveigné. It builds on an initial python implementation by [Pedro Alcocer](https://github.com/pealco).
 
-### References
+Only CCA, SNS, DSS, STAR, ZapLine and robust detrending have been properly tested so far. TSCPA may give inaccurate results due to insufficient testing (contributions welcome!)
 
 If you use this code, you should cite the relevant methods from the original articles:
 
@@ -50,8 +50,8 @@ If you use this code, you should cite the relevant methods from the original art
     NeuroImage, 172, 206–216. https://doi.org/10.1016/j.neuroimage.2018.01.033
 [4] de Cheveigné, A. (2016). Sparse time artifact removal.
     Journal of Neuroscience Methods, 262, 14–20. https://doi.org/10.1016/j.jneumeth.2016.01.005
-[5] de Cheveigné, A., & Parra, L. C. (2014). Joint decorrelation, a versatile tool for multichannel data
-    analysis. NeuroImage, 98, 487–505. https://doi.org/10.1016/j.neuroimage.2014.05.068
+[5] de Cheveigné, A., & Parra, L. C. (2014). Joint decorrelation, a versatile tool for multichannel
+    data analysis. NeuroImage, 98, 487–505. https://doi.org/10.1016/j.neuroimage.2014.05.068
 [6] de Cheveigné, A. (2012). Quadratic component analysis.
     NeuroImage, 59(4), 3838–3844. https://doi.org/10.1016/j.neuroimage.2011.10.084
 [7] de Cheveigné, A. (2010). Time-shift denoising source separation.
@@ -65,20 +65,28 @@ If you use this code, you should cite the relevant methods from the original art
 
 ```
 
-## Python implementation of Artifact subspace reconstruction (ASR)
+### 2. Artifact subspace reconstruction (ASR)
 
 The base code is inspired from the original [EEGLAB inplementation](https://github.com/sccn/clean_rawdata) [1], while the riemannian variant [2] was adapted from the [rASR toolbox](https://github.com/s4rify/rASRMatlab) by Sarah Blum.
 
-### References
-
 If you use this code, you should cite the relevant methods from the original articles:
 
 ```sql
-[1] Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., et al. (2015). Real-time
-    neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Bio-Med. Eng. 62, 2553–2567.
-    https://doi.org/10.1109/TBME.2015.2481482
+[1] Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., et al. (2015).
+    Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Bio-Med.
+    Eng. 62, 2553–2567. https://doi.org/10.1109/TBME.2015.2481482
 [2] Blum, S., Jacobsen, N., Bleichner, M. G., & Debener, S. (2019). A Riemannian modification of
     artifact subspace reconstruction for EEG artifact handling. Frontiers in human neuroscience,
     13, 141.
+```
 
+### 3. Rhythmic entrainment source separation (RESS)
+
+The code is based on [Matlab code from Mike X. Cohen](https://mikexcohen.com/data/) [1]
+
+If you use this, you should cite the following article:
+
+```sql
+[1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source separation: Optimizing analyses
+    of neural responses to rhythmic sensory stimulation. Neuroimage, 147, 43-56.
 ```
diff --git a/doc/index.rst b/doc/index.rst
@@ -23,6 +23,7 @@ Contents
    ~meegkit.cca
    ~meegkit.dss
    ~meegkit.detrend
+   ~meegkit.ress
    ~meegkit.sns
    ~meegkit.star
    ~meegkit.tspca

diff --git a/doc/modules/meegkit.asr.rst b/doc/modules/meegkit.asr.rst
@@ -1,30 +1,20 @@
-meegkit.asr
+meegkit.asr
 ===========
 
 .. automodule:: meegkit.asr
 
-
-
    .. rubric:: Functions
 
    .. autosummary::
-   
+
       asr_calibrate
       asr_process
       clean_windows
-
-
 
-
-
    .. rubric:: Classes
 
    .. autosummary::
-   
+
       ASR
-
-
 
-
-
-
+
diff --git a/doc/modules/meegkit.cca.rst b/doc/modules/meegkit.cca.rst
@@ -1,10 +1,14 @@
-meegkit.cca
+meegkit.cca
 ===========
 
 .. automodule:: meegkit.cca
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -25,4 +29,7 @@ meegkit.cca
 
 
 
-
+
+
+
+
diff --git a/doc/modules/meegkit.detrend.rst b/doc/modules/meegkit.detrend.rst
@@ -1,10 +1,14 @@
-meegkit.detrend
+meegkit.detrend
 ===============
 
 .. automodule:: meegkit.detrend
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -21,4 +25,7 @@ meegkit.detrend
 
 
 
-
+
+
+
+
diff --git a/doc/modules/meegkit.dss.rst b/doc/modules/meegkit.dss.rst
@@ -1,10 +1,14 @@
-meegkit.dss
+meegkit.dss
 ===========
 
 .. automodule:: meegkit.dss
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -21,4 +25,7 @@ meegkit.dss
 
 
 
-
+
+
+
+
diff --git a/doc/modules/meegkit.ress.rst b/doc/modules/meegkit.ress.rst
@@ -0,0 +1,23 @@
+meegkit.ress
+============
+
+.. automodule:: meegkit.ress
+
+   .. rubric:: Functions
+
+   .. autosummary::
+
+      RESS
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/doc/modules/meegkit.sns.rst b/doc/modules/meegkit.sns.rst
@@ -1,10 +1,14 @@
-meegkit.sns
+meegkit.sns
 ===========
 
 .. automodule:: meegkit.sns
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -21,4 +25,7 @@ meegkit.sns
 
 
 
-
+
+
+
+
diff --git a/doc/modules/meegkit.star.rst b/doc/modules/meegkit.star.rst
@@ -1,10 +1,14 @@
-meegkit.star
+meegkit.star
 ============
 
 .. automodule:: meegkit.star
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -19,4 +23,7 @@ meegkit.star
 
 
 
-
+
+
+
+
diff --git a/doc/modules/meegkit.tspca.rst b/doc/modules/meegkit.tspca.rst
@@ -1,10 +1,14 @@
-meegkit.tspca
+meegkit.tspca
 =============
 
 .. automodule:: meegkit.tspca
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -20,4 +24,7 @@ meegkit.tspca
 
 
 
-
+
+
+
+
diff --git a/doc/modules/meegkit.utils.rst b/doc/modules/meegkit.utils.rst
@@ -5,6 +5,7 @@ meegkit.utils
 
    .. autosummary::
 
+    auditory
     covariances
     denoise
     matrix
@@ -15,6 +16,17 @@ meegkit.utils
 
 ----
 
+Auditory
+--------
+.. automodule:: meegkit.utils.auditory
+
+   .. autosummary::
+
+
+|
+
+----
+
 Covariances
 -----------
 .. automodule:: meegkit.utils.covariances