Description
This aims at keeping track of the development related to the Google summer of code 2016 on decoding analyses.
We have quite a lot ongoing PRs, so I thought I would try to organize them here to keep track of the big picture. I'll edit the post along the issue along the way.
The aim is to make transformers that follow the sklearn API:
pipe = make_pipeline(
CSP(sfreq=200, None, 30),
TimeFrequency(),
SlidingEstimator(make_pipeline(StandardScaler(), LogisticRegression())
)
score = cross_val_score(X=epochs.get_data(), y=epochs.event[:, 2])
For now, we're focusing on sklearn integration, not high level features (plotting, get_coefs_ etc).
-
Spatial filters:
- FIX: XdawnTransformer: [MRG]: Xdawn Transformer compatible with sklearn #3245
- FIX: Xdawn refactoring: MRG: Refactor Xdawn #3425
- FIX: CSP renaming of
epochs_dataintoX[MRG] multiclass CSP #3485 - ENH: CSP adding multiclass support see ENH: CSP support for n_classes > 2 #3484, [MRG] multiclass CSP #3485
- ENH: CSP uwedge multiclass + example ENH: CSP multiclass uwedge implementation + examples #3495
- ENH: Unsupervised spatial filter [MRG] Spatial Filter #3447
- ENH: EMS: ENH: EMS with sklearn object? #3427, MRG: EMS transformer with sklearn API #3446
- ENH: CSP transform into power or time course or CSP transform doesn't comply with sklearn API #3583 MRG: add param to CSP to transform into average power or time course #3586
- ENH: Mixin class to plot patterns_ and filters_? see BUG: time domain of the plot_pattern exceed range #3424
- FIX: Move Xdawn to decoding?
-
Freq - time/freq transformers:
- FIX: TemporalFilter: needs refactoring from
mne.decoding.FilterEstimatorto pass explicit args and notinfo+ BUG in FilterEstimator: BUG with FilterEstimator [DONT MERGE] #3395, ENH: Filterer for decoding #3471, [MRG+1] Created class Filterer and deprecated FilterEstimator #3472 - FIX: PSDEstimator
- ENH: TimeFrequencyDecomposer [MRG] TimeFrequency transformer for sklearn pipeline #3488
- ENH: SPoC [MRG+1] Add SPoC spatial filtering for continuous target decoding #4144
- Time Frequency Decoding object based on covariances / CSP ...
- Power Decoding Estimator based on covariances / CSP: [MRG] Frequency CSP Example #4138 (example, but need to be converted in object)
- FIX: TemporalFilter: needs refactoring from
-
Continuous signals (raw not epochs)
- FIX: Xdawn overlap: to be integrated with the RERP code ENH: refactor Xdawn and linear_regression_raw #2332, also see https://github.com/mne-tools/mne-python/blob/master/mne/stats/tests/test_regression.py#L124, PR in WIP: refactor XDawn to use rERP #3563
- FIX: ReceptiveField: started in WIP: sklearn-style encoding / modularizing encoding pipelines #3310, done in [MRG+2] adding receptive field module #3728
-
FIX: combine rERP and STRF: e.g. float delays instead of list, discrete vs continuous regressors etc -
Optimize ReceptiveField for continuous regression
-
Search lights
- ENH: SlidingEstimator and GeneralizingEstimator: MRG: Add search and generalization lights #3381 MRG+1: refactor time decoding & temporal generalization #4103
- ENH: n-dimensional search light [MRG]: allow n_dimensional search lights #3481
- FIX: Refactor TimeDecoding & GeneralizationAcrossTime MRG+1: refactor time decoding & temporal generalization #4103
- ENH: 'scoring' param in SearchLight-like object ENH: specify scoring in search light #3475, MRG: Functionality to change scoring method of searchlight #3502, MRG: scoring in generalization light #3833 MRG+1: refactor time decoding & temporal generalization #4103
- ENH: option to warm_start from one estimator to the next
- cross_val_multiscore MRG+1: refactor time decoding & temporal generalization #4103
- window_size and step parameter in SlidingEstimator
-
Preprocessing
- ENH: Scaler: scale channels over all time their corresponding time points to deal with issues related to mix channel types needs refactoring to comply to
XyAPI - ENH: Vectorizer: to pass from n-D
Xto 2DXto be reviewed in [MRG] Vectorizer class to help chain MNE transformers by converting o/p into 2D #3409
- ENH: Scaler: scale channels over all time their corresponding time points to deal with issues related to mix channel types needs refactoring to comply to
-
Examples:
- RSA MRG: RSA example #3923
- TimeFrequency Example MRG+1: Time Frequency CSP Example #4115
- PSD example [MRG] Frequency CSP Example #4138
- get_coef for SlidingEstimator
- Denoise tutorial
-
Frequency generalization [WIP] Datasets from the BNCI database #4019 - UnsupervisedSpatialFilter example
-
Other:
-
partial_fit ENH: partial_fit for linear models #3483, WIP: partial_fit in search light #3591 -
get_coef(pipeline)to retrieve and/or invert_transform linear coefficients if they exist
-