For example, [PERSON] [14] utilized the continuous Wavelet transform (CWT) to analyze the EEG signals and detect tonic cold pain. In particular, the CWT was employed to construct a time-frequency representation of the EEG signals. Then, a set of higher-order spectral features were extracted and used to train and evaluate the performance of four different classifiers, namely the quadratic discriminant analysis (QDA), Mahalanobis (MAH), k-nearest neighbor (k-NN), and SVM with RBF kernel classifiers. The performance of these classifiers was evaluated using EEG signals acquired from 17 healthy subjects while holding a plastic bottle with iced water until the pain became unbearable. The experimental results indicate that the best classification accuracy was achieved using the MAH classifier. In another study, [PERSON] et al. [15] proposed a tonic cold pain detection approach that utilizes the discrete Wavelet transform (DWT) to analyze the EEG signals and detect pain. This approach was evaluated using EEG signals that were acquired from 24 healthy subjects while submerging their hands in iced water to stimulate tonic cold pain. The DWT was applied to the recorded EEG signals, and a set of features were extracted from the computed DWT coefficients. The extracted features were used to train a SVM classifier with RBF kernel to distinguish between the EEG signals that correspond to the no-pain and pain states. Despite the promising results achieved using the Wavelet transform analysis of the EEG signals, including CWT and DWT, the time-frequency representations obtained using the Wavelet transform are characterized by a nonuniform resolution across the time-frequency plane (TFP) [16], which can increase the difficulty to interpret the obtained time-frequency representation of the EEG signals [16; 17]. To address the limitations of the previously described EEG-based pain detection approaches, we propose an EEG-based pain detection approach that employs a quadratic time-frequency distribution, namely the Choi-Williams distribution (CWD), to analyze the EEG signals. Compared to other time-frequency analyses, such as the Wavelet transform, the QTFDs are considered invariant to time and frequency shifts [16; 18; 19]. Therefore, the use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals that can capture the time-varying spectral characteristics of the EEG signals. To reduce the dimensionality of the TFRs constructed for the EEG signals, we have extracted 12 time-frequency features from the constructed TFR of the EEG signals. These extracted time-frequency features are used to train a support vector machine (SVM) classifier to distinguish between EEG signals that are associated with pain and no-pain states. To evaluate the performance of the proposed approach, we have recorded the EEG signals for 24 healthy subjects who volunteered to participate in a tonic cold pain stimulation experiment. Moreover, we have developed two performance evaluation procedures--channel- and feature-based analyses--to evaluate the effect of the selected EEG channels and time-frequency features on the capability of detecting pain. The results reported in the current study demonstrate the capability of our proposed approach to detect tonic cold pain using EEG signals. The main objective of the current study is to contribute to ongoing research efforts developing objective pain detection systems based on time-frequency analysis of the EEG-based pain detection. In particular, we have introduced an EEG-based system that employs a set of time-frequency features that are extracted from a QTFD-based representation to discriminate between the no-pain and pain classes. In fact, we hypothesize that analyzing the EEG signals using the CWD, which is considered as a QTFD, can improve the accuracy of pain detection compared with other previously used frequency-domain and time-frequency domain analyses, such as the PSD-based analysis and the wavelet transform (WT) analysis.