Cross-validators (CV) take a classifier (CL), a datasource (DS) and optionally feature preprocessor (FP) objects, and they run a cross-validation decoding scheme by training and testing the classifier with data generated from the datasource object (and possibly fed through the feature pre-processing first). Objects that are cross-validators must implement the following method:
DECODING_RESULTS = cv.run_cv_decoding
- This methods uses a datasource (DS) to generate training and test splits of the data, optionally applies feature preprocessors (FP) to the training and test data, sends the training data to a classifier (CL) which learns the relationship between the data and the labels, and then tests the classifier using the test data generated by the datasource (note that a datasource and a classifier must be set prior to running this method). This method may repeat the cross-validation decoding procedure multiple times by generating different data splits from the datasource in order to get more robust measures of the decoding accuracy.
The Neural Decoding Toolbox (version 1.0) comes with following classifier objects: