EEG-Based tonic cold pain characterization using wavelet higher order spectral features

A novel approach in tonic cold pain characterization, based on electroencephalograph (EEG) data analysis using wavelet higher order spectral (WHOS) features, is presented here. The proposed WHOS-based feature space extends the relative power spectrum-based (phase blind) approaches reported so far a step forward; this is realized via dynamic monitoring of the nonlinerities of the EEG brain response to tonic cold pain stimuli by capturing the change in the underlying quadratic phase coupling at the bifrequency wavelet bispectrum/bicoherence domain due to the change of the pain level. Three pain characterization scenarios were formed and experimentally tested involving WHOS-based analysis of EEG data, acquired from 17 healthy volunteers that were subjected to trials of tonic cold pain stimuli. The experimental and classification analysis results, based on four well-known classifiers, have shown that the WHOS-based features successfully discriminate relax from pain status, provide efficient identification of the transition from relax to mild and/or severe pain status, and translate the subjective perception of pain to an objective measure of pain endurance. These findings seem quite promising and pave the way for adopting WHOS-based approaches to pain characterization under other types of pain, e.g., chronic pain and various clinical scenarios.