Modified Feature Selection for Improved Classification of Resting-State Raw EEG Signals in Chronic Knee Pain

TL;DR

This paper introduces a modified feature selection algorithm for EEG data that significantly improves the accuracy of chronic knee pain prediction, aiding in automatic diagnosis.

Contribution

A novel modified Sequential Floating Forward Selection algorithm enhances feature selection for EEG-based pain classification, outperforming existing methods.

Findings

Achieved 97.5% test accuracy in pain prediction

Selected a compact, effective connectivity feature subset

Outperformed benchmark feature selection methods

Abstract

\textit{Objective:} Diagnosing pain in research and clinical practices still relies on self-report. This study aims to develop an automatic approach that works on resting-state raw EEG data for chronic knee pain prediction. \textit{Method:} A new feature selection algorithm called ``modified Sequential Floating Forward Selection'' (mSFFS) is proposed. The improved feature selection scheme can better avoid local minima and explore alternative search routes. \textit{Results:} The feature selection obtained by mSFFS 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\%. \textit{Conclusion:} The improved feature selection searches out a compact, effective subset of connectivity features that produces competitive performance on chronic knee pain prediction. \textit{Significance:} We have shown that an automatic approach can be employed to find a compact connectivity feature set that effectively predicts chronic knee pain from EEG. It may shed light on the research of chronic pains and lead to future clinical solutions for diagnosis and treatment.