different types of pain stimuli, tonic pain stimuli are widely used due to their high induced effects compared to the other types of pain stimuli. In particular, tonic pain can cause impairments in the performance of visual tasks and memory [3; 4]. Moreover, various studies have indicated that tonic pain can simulate clinical pain better than other types of pain stimuli [8]. To record brain activities that are generated in response to an exogenous pain stimulus, researchers have employed several neuroimaging modalities, such as the electrocorticographic (ECoG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). The EEG modality, which records the electrical potentials produced at different locations in the brain in response to an exogenous pain stimulus, is considered the most commonly used modality for designing objective pain detection systems. This can be attributed to several properties that are associated with the EEG modality, including the high temporal resolution, low-cost, and noninvasive nature [5; 6]. Nonetheless, pain detection based on EEG signals analysis is considered challenging due to the nonstationarity nature of the EEG signals, low spatial resolution, and low signal-to-noise (SNR) ratio [7]. Over the past decade, several approaches have been proposed to detect stimulated pain based on EEG signals analysis. For example, [PERSON] et al. [8] studied the relation between the properties of the peak alpha frequency (PAF) of the EEG signals and the subjective perception of tonic heat pain. The employed EEG signals were recorded for 18 healthy subjects during the application of tonic heat pain stimulus. The results indicate a correlation between the PAF and the subjective perception of tonic pain, which suggests the possibility of utilizing the properties of the PAF to characterize pain. [PERSON] and [PERSON] [9] proposed an approach to detect tonic heat pain using power spectral density (PSD)-based features that were extracted from the EEG signals. The extracted features were used to construct a support vector machine (SVM) classifier to distinguish between EEG signals that correspond to the no-pain and pain classes, respectively. The performance of the proposed system was evaluated using EEG signals that were acquired from nine healthy subjects during the application of tonic heat pain stimulus. [PERSON] et al. [10] performed a frequency-domain EEG source analysis to study brain responses to tonic cold pain stimulus. In particular, the utilized EEG signals were recorded for 26 healthy subjects under two conditions--absence or presence of a tonic cold pain stimulus. The results show that the variations in the power associated with different frequency bands of the EEG signals can be used to analyze the perception mechanism of tonic cold pain. In another study, [PERSON] and [PERSON] [11] proposed an approach that employs the wavelet coherency to estimate the tonic cold pain level. In particular, first- and second-order statistical features were extracted from the wavelet coherency of the EEG signals and used to construct two classifiers-- an SVM classifier with radial basis function (RBF) kernel and a hidden Markov model (HMM) classifier. The results indicate that the classification performance obtained using the SVM classifier outperform the classification results obtained using the HMM classifier. The previously described studies were focused on analyzing the EEG signals in the frequency-domain without considering the nonstationarity nature of these signals. The nonstationarity nature of the EEG signals imposes the requirement of analyzing these signals in the joint time-frequency domain to capture the spectral variations of the EEG signals over time. In this regard, few research groups have recently investigated the use of time-frequency analysis methods, such as wavelet transform [12; 13], to characterize the EEG signals and detect tonic pain.