A TimeFrequency 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 timefrequency distribution (QTFD), namely the WignerVille distribution (WVD). The use of the WVD enables to construct a timefrequency representation (TFR) of the EEG signals that characterizes the timevarying spectral components of the EEG signals. To reduce the dimensionality of the constructed WVDbased TFR of the EEG signals, we have extracted 12 timefrequency features that quantify the energy distribution of the EEG signals in the constructed WVDbased TFR. The extracted timefrequency features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the nopain 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 nopain and pain classes.

Local Gaussian Process Features for Clinical Sensor Time Series Time series obtained from clinical sensors such as EEG and Accelerometers often contain mixtures of complex signals that can be difficult to interpret and require advanced methods for automated analysis. In this work, we introduce a flexible and extensible method for transforming clinical time series into new, simpler features that may aid in automated analysis and interpretation. The method is based on information extracted from locallytrained Gaussian Processes. In this work, we describe the procedure, illustrate the idea on contrived data and demonstrate its effectiveness at improving existing methods for both epilepsy detection and activity classification. We show that our feature extraction technique generalizes, and can be used as a preprocessing step for arbitrary machine learning and statistical methods. We further illustrate that the techniques evaluated in our experiments form only a small subset of a very broad class of feature extraction methods on arbitrary data that might form the basis for further study.