A Time-Frequency Distribution Based Approach for Detecting Tonic Cold Pain using EEG Signals

In this paper, we present a new pain detection approach that analyzes the electroencephalography (EEG) signals using a quadratic time-frequency distribution (QTFD), namely the Wigner-Ville distribution (WVD). The use of the WVD enables to construct a time-frequency representation (TFR) of the EEG signals that characterizes the time-varying spectral components of the EEG signals. To reduce the dimensionality of the constructed WVD-based TFR of the EEG signals, we have extracted 12 time-frequency features that quantify the energy distribution of the EEG signals in the constructed WVD-based TFR. The extracted time-frequency features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To assess the performance of our proposed pain detection approach, we have recorded the EEG signals for 24 participants under tonic cold pain stimulus. The experimental results show that our proposed approach achieved an average classification accuracy of 83.4% in distinguishing between the no-pain and pain classes.