Granger Causality-Based Pain Classification Using EEG Evoked by Electrical Stimulation Targeting Nociceptive Aδ and C Fibers

Abstract

Classification of pain levels from evoked electroencephalography (eEEG) has been achieved for the purpose of objective assessment of pain. However, differentiation of acute and chronic pain resulting from the activation of nociceptive nerves, the Aδ and C fibers, respectively, has remained unsolved, mainly due to the lack of effective features. Granger causality (GC) was applied to show the role of specific brain areas in the differentiation between pain and tactile sensation but yet was used to differentiate nociceptive fiber activations. The purpose of this study is two-fold: first, to develop pain perception classification systems using GC as features, and second, to identify causal connectivity for effective differentiation of nociceptive fiber activations. The eEEG responding to three stimulation waveforms, 1 Hz square waves, 5 Hz sine waves, both targeting C fibers, and 250 Hz sine waves, targeting Aδ, were recorded. Three GC categories based on channel areas, frequency bands, and stimulation waveforms were computed from the eEEG and used as features to classify nociceptive fiber activations and pain levels. It was found that GCs in the alpha band produced the highest average classification accuracy of 99.71%, followed by the gamma band, with 98.66%. Meanwhile, the GC feature contributing most to differentiating Aδ and C was found in the alpha band. The GC flow computed from the results of 5 Hz and 250 Hz stimulation had an important role in the prediction of mild pain and maximum pain, respectively. These results demonstrated the high potential of GC as features of pain-related information.

EEG Interchannel Causality to Identify Source/Sink Phase Connectivity Patterns in Developmental Dyslexia

“…First employed in the 1980s in the economics field, Granger causality is a statistical hypothesis test that has been used to produce good results in a wide range of other fields 10 . Neuroscience research has applied it to EEG measurements, producing findings on brain activity in emotion recognition 27 , Vagus nerve stimulation 28 , and pain perception 29 . Connectivity based on causality implies cause-effect relationships between various areas of the brain, but these are not necessarily bidirectional.…”

Electrophysiological markers for anticipatory processing of nocebo-augmented pain

“…A future study directly comparing SPN for both placebo and nocebo effects could investigate this possibility, and a study employing an acute measure of state fear instead of trait fear could shed light on any potential link between fear of pain and SPN. While Granger Causality has previously been used as a means of characterizing different intensities of pain [23], and differences in pain processing between individuals prone to migraines and those not [24], this was its first use for investigating the anticipation of pain, and how that process may change when pain is shaped by a nocebo effect. We had hypothesized that expectations regarding the upcoming pain stimulus would be communicated from frontal to temporoparietal regions, where integration with the bottom-up sensory inputs would shape the pain experience [3,18,47].…”

“…Granger Causality, a measure of how past events in time series X may predict future events in time series Y, offers a method of quantifying functional connectivity, or the transfer of information between regions in the brain [20][21][22]. This method has previously been applied to classify the electrophysiological signature of different pain intensities [23], and differences in pain processing between individuals with and without chronic migraines [24], but not to study nocebo, placebo, or other expectancy effects related to pain. By measuring functional connectivity during the experience of nocebo effects on pain, we can build on previous work that identified regions of interest for nocebo effects, and observe how these regions interact to shape pain sensations.…”

A Temporospatial Study of Sympathetic Skin Response and Electroencephalogram in Oral Mucosa Thermal Perception

“…The stimulation time in our study was shorter than that in continuous stimulation. There were many reasons for the differences between studies such as stimulation protocol, stimulus intensity, and electrode locations (Tripanpitak et al, 2021). In future research, we will make full use of the 32-channel data collected from the perspective of the brain network to analyze the information flow between different nodes in detail, including the characteristics of the brain network, such as the flow gain, clustering coefficient, and characteristic path length, to investigate what difference between different temperature and the EEG brain network connection to thermal stimuli (Bunk et al, 2018).…”