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Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction.

Bai Y, Huang G, Tu Y, Tan A, Hung YS, Zhang Z - Front Comput Neurosci (2016)

Bottom Line: Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity.In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered.Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological UniversitySingapore, Singapore; School of Data and Computer Science, Sun Yat-Sen UniversityLouvain, Belgium.

ABSTRACT
An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.

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Related in: MedlinePlus

(A) Relationship between pain ratings and RMSP (from one participant). Colored dots represent mean ± SD of RMSP averaged across trials at different level of pain perception. The red line represents the fitted global linear model, while the blue lines represent the fitted two-piecewise linear model. (B) Comparison of MSE (mean ± SD) of all participants between two fitting models.
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Figure 2: (A) Relationship between pain ratings and RMSP (from one participant). Colored dots represent mean ± SD of RMSP averaged across trials at different level of pain perception. The red line represents the fitted global linear model, while the blue lines represent the fitted two-piecewise linear model. (B) Comparison of MSE (mean ± SD) of all participants between two fitting models.

Mentions: Figure 2A shows the relationship between pain rating and the magnitude of pEEG (mean ± SD) of one participant. Overall, pain rating and RMSP are positively related, but RMSP does not increase significantly when the subjective pain ratings is ≤ 4 (referred to as “low-pain”); when the subjective pain rating is >4 (referred to as “high-pain”), RMSP is linearly increased with pain ratings. MSE of the global linear model (red line) or the two-piecewise linear model (blue line) was adopted to measure the accuracy of fitting, as shown in Figure 2B. It can be seen from the group-level results in Figure 2B that, the fitting error of the piecewise linear model is significantly smaller than that of the global linear model. Therefore, the piecewise linear model can better describe the relationship between pain perception and RMSP. The nonlinear relationship motivates us to develop the two-stage pain prediction (i.e., to classify low- and high-pain first, then to predict the pain rating for high-pain trials only).


Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction.

Bai Y, Huang G, Tu Y, Tan A, Hung YS, Zhang Z - Front Comput Neurosci (2016)

(A) Relationship between pain ratings and RMSP (from one participant). Colored dots represent mean ± SD of RMSP averaged across trials at different level of pain perception. The red line represents the fitted global linear model, while the blue lines represent the fitted two-piecewise linear model. (B) Comparison of MSE (mean ± SD) of all participants between two fitting models.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4829613&req=5

Figure 2: (A) Relationship between pain ratings and RMSP (from one participant). Colored dots represent mean ± SD of RMSP averaged across trials at different level of pain perception. The red line represents the fitted global linear model, while the blue lines represent the fitted two-piecewise linear model. (B) Comparison of MSE (mean ± SD) of all participants between two fitting models.
Mentions: Figure 2A shows the relationship between pain rating and the magnitude of pEEG (mean ± SD) of one participant. Overall, pain rating and RMSP are positively related, but RMSP does not increase significantly when the subjective pain ratings is ≤ 4 (referred to as “low-pain”); when the subjective pain rating is >4 (referred to as “high-pain”), RMSP is linearly increased with pain ratings. MSE of the global linear model (red line) or the two-piecewise linear model (blue line) was adopted to measure the accuracy of fitting, as shown in Figure 2B. It can be seen from the group-level results in Figure 2B that, the fitting error of the piecewise linear model is significantly smaller than that of the global linear model. Therefore, the piecewise linear model can better describe the relationship between pain perception and RMSP. The nonlinear relationship motivates us to develop the two-stage pain prediction (i.e., to classify low- and high-pain first, then to predict the pain rating for high-pain trials only).

Bottom Line: Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity.In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered.Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological UniversitySingapore, Singapore; School of Data and Computer Science, Sun Yat-Sen UniversityLouvain, Belgium.

ABSTRACT
An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.

No MeSH data available.


Related in: MedlinePlus