Chronic pain detection from resting-state raw EEG signals using improved feature selection

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

Resting-state EEG sex classification using selected brain connectivity representation

Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex prediction of resting-state EEG signals. We have found that the brain connectivity represented by the coherence between certain sensor channels are good predictors of sex.