Characteristics of Evoked Potential Multiple EEG Recordings in Patients with Chronic Pain by Means of Parallel Factor Analysis

Abstract

This paper presents an alternative method, called as parallel factor analysis (PARAFAC) with a continuous wavelet transform, to analyze of brain activity in patients with chronic pain in the time-frequency-channel domain and quantifies differences between chronic pain patients and controls in these domains. The event related multiple EEG recordings of the chronic pain patients and non-pain controls with somatosensory stimuli (pain, random pain, touch, random touch) are analyzed. Multiple linear regression (MLR) is applied to describe the effects of aging on the frequency response differences between patients and controls. The results show that the somatosensory cortical responses occurred around 250 ms in both groups. In the frequency domain, the neural response frequency in the pain group (around 4 Hz) was less than that in the control group (around 5.5 Hz) under the somatosensory stimuli. In the channel domain, cortical activation was predominant in the frontal region for the chronic pain group and in the central region for controls. The indices of active ratios were statistical significant between the two groups in the frontal and central regions. These findings demonstrate that the PARAFAC is an interesting method to understanding the pathophysiological characteristics of chronic pain.

Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time–frequency–channel domain

“… 13 Wang et al found that administering moderately painful and unpainful electrical stimulation on the dominant index finger resulted in a dominant central response at 250 milliseconds poststimulus with a dominant frequency response of 5.5 Hz after Parafac decomposition. 19 In this study, similar neural signatures were found after painful electrical stimulation of different painful intensities, except for the frequency domain. A possible explanation for the different frequency bands in both studies could be the predefined knowledge of the participants.…”

“… 38 , 39 Compared to healthy participants, chronic pain patients may have more frontal cortical activation and a lower response frequency. 19 Evaluating the signatures of experimentally induced pain and comparing them to the signatures of healthy participants may provide more insight into the altered brain-processing mechanisms of chronic pain patients and may possibly have predictive value for the development of chronic pain. This could help in answering an important question in clinical practice to predict in which patients a certain treatment or surgery will be successful and which patients will be likely to develop chronic pain.…”

“… 13 Certain models, such as the Parafac model, provide the opportunity to split the time–frequency information into separate domains. 15 – 19 This helps to overcome the previously described challenge of predefining certain regions of interest, since these models deliver separated-component information of the measured brain response. 18 …”

Tensor decomposition of TMS-induced EEG oscillations reveals data-driven profiles of antiepileptic drug effects

“…Tensor decomposition has also been used in artefact rejection and estimation of seizure onset zone 18,19 . Other applications include localisation of EEG sources 20 , connectivity estimation 21 , brain computer interfaces 22,23 , and feature extraction in clinical and psychological studies 24–26 . Tensor decomposition are also useful when fusing EEG with other datasets 27–29 .…”

The Frontal Area with Higher Frequency Response Is the Principal Feature of Laser-Evoked Potentials in Rats with Chronic Inflammatory Pain: A Parallel Factor Analysis Study

“…The parallel factor analysis (PARAFAC) is a method that could extract features in the time–frequency–channel domain simultaneously from original multichannel EEG data ( 15 ). It takes into account the frequency of oscillations in certain time periods among all the recording channels ( 16 ) and has been successfully applied to detect abnormal oscillatory activity in epilepsy and Alzheimer’s disease ( 17 ).…”