Decoding an Individual's Sensitivity to Pain from the Multivariate Analysis of EEG Data

Abstract

The perception of pain is characterized by its tremendous intra- and interindividual variability. Different individuals perceive the very same painful event largely differently. Here, we aimed to predict the individual pain sensitivity from brain activity. We repeatedly applied identical painful stimuli to healthy human subjects and recorded brain activity by using electroencephalography (EEG). We applied a multivariate pattern analysis to the time-frequency transformed single-trial EEG responses. Our results show that a classifier trained on a group of healthy individuals can predict another individual's pain sensitivity with an accuracy of 83%. Classification accuracy depended on pain-evoked responses at about 8 Hz and pain-induced gamma oscillations at about 80 Hz. These results reveal that the temporal-spectral pattern of pain-related neuronal responses provides valuable information about the perception of pain. Beyond, our approach may help to establish an objective neuronal marker of pain sensitivity which can potentially be recorded from a single EEG electrode.

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

“…Machine learning and statistical techniques are often used to identify patterns in these data, and are optimized to jointly predict patient status, the experience of pain, analgesia, and other outcomes. These approaches have been successfully used to decode some aspects of stimulus-evoked acute pain from patterns of brain activity, at least to some extent 51,[61][62][63][64][65][66][67][68][69][70][71] …”

“…With this approach, one can determine the stimulus-evoked brain responses that differ between patients with chronic pain and healthy individuals, or between responses elicited by stimuli applied to affected and unaffected areas of the same patient [83][84][85][86] . Alternatively, brain responses that correlate with pain intensity 79,82,[87][88][89][90][91] , or perceptrelated brain activity that fluctuates in synchrony with the experience of pain 31,69,89,[92][93][94][95][96] can be identified. Association of activity with the experience of pain is particularly important in chronic pain, because an evoked pain response can be out of sync with or completely disconnected from the timing and duration of the applied stimulus 89 Machine learning is an analysis approach that exploits the ability of computers to learn from, and make predictions from, different kinds of data 125 .…”

Quantifying the time for accurate EEG decoding of single value-based decisions

“…The first one is logistic regression (Parra et al, 2002(Parra et al, , 2005, an algorithm which has been widely applied for studying perceptual decision-making (Philiastides and Sajda, 2006;Ratcliff et al, 2009) and in single-trial decoding in general (Brandmeyer et al, 2013;Farquhar and Hill, 2013). The second is Support Vector Machines (SVM), a powerful machine learning technique which has been used in various decoding applications (SVM; (Rieger et al, 2008;Salvaris and Sepulveda, 2009;Schulz et al, 2012;Taghizadeh-Sarabi et al, 2014)). …”

Pain Level Measurement Using Discrete Wavelet Transform

“…It seems that the lack of clear and consistent answer to this question is related to the methods used for analysis. All of these studies Schulz et.al studied individual pain sensitivity prediction using EEG and reported 83% accuracy [9]. Panavaranan and Wongsawat used a fuzzy logic intelligent method in order to classify the pain level and reach to 96.97% of accuracy [10].…”