  We present an automatic approach that works on resting-state raw EEG data for
chronic pain detection. A new feature selection algorithm - modified Sequential
Floating Forward Selection (mSFFS) - is proposed. The improved feature
selection scheme is rather compact but displays better class separability as
indicated by the Bhattacharyya distance measures and better visualization
results. It also outperforms selections generated by other benchmark methods,
boosting the test accuracy to 97.5% and yielding a test accuracy of 81.4% on an
external dataset that contains different types of chronic pain

Machine Learning