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Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

Hu L, Zhang ZG, Mouraux A, Iannetti GD - Neuroimage (2015)

Bottom Line: Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations.ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding.This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing, China; Department of Neuroscience, Physiology and Pharmacology, University College London, UK. Electronic address: huli@swu.edu.cn.

No MeSH data available.


Related in: MedlinePlus

Example of a single-trial TFD modelled using both TF-MLR and TF-MLRd.Left panel: A single-trial TFD of a laser-elicited EEG response recorded at electrode Cz. Middle panel: Time-frequency response features (ERP, ERD and ERS) fitted using TF-MLR and TF-MLRd. Note that the information-of-interest (i.e., the ERP, ERD and ERS responses) is preserved, while the information-of-no-interest (e.g., background EEG noise) is largely removed, thus increasing the response SNR. Right panel: The sum of the fitted ERP, ERD and ERS responses constituted the modelled single-trial TFD. TF-MLRd provides a significantly better fit of single trials than TF-MLR (F = 126, p < 0.001).
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f0025: Example of a single-trial TFD modelled using both TF-MLR and TF-MLRd.Left panel: A single-trial TFD of a laser-elicited EEG response recorded at electrode Cz. Middle panel: Time-frequency response features (ERP, ERD and ERS) fitted using TF-MLR and TF-MLRd. Note that the information-of-interest (i.e., the ERP, ERD and ERS responses) is preserved, while the information-of-no-interest (e.g., background EEG noise) is largely removed, thus increasing the response SNR. Right panel: The sum of the fitted ERP, ERD and ERS responses constituted the modelled single-trial TFD. TF-MLRd provides a significantly better fit of single trials than TF-MLR (F = 126, p < 0.001).

Mentions: Based on the fitted single-trial TFD (Fig. 5, top panel), we calculated, for each TF-feature, the correlation coefficient (CC1, CC2 and CC3 for ERP, ERD and ERS, respectively) between the fitted single-trial TFD and the thresholded TFD obtained from PCA decomposition with Varimax rotation. Single-trial ERP and ERS magnitudes were finally obtained by calculating the mean of the 20% of points (relative to all the non-zero points in the thresholded TFD for each TF-feature [Fig. 1, right panel], the same hereinafter) displaying the highest increase (if CC1 > 0 or CC3 > 0, i.e., a positive fit) or the highest decrease (if CC1 < 0 or CC3 < 0, i.e., a negative fit), respectively. In contrast, single-trial ERD magnitude was obtained by calculating the mean of the 20% of points displaying the highest decrease (if CC2 > 0, i.e., a positive fit), or the highest increase (if CC2 < 0, i.e., a negative fit). Finally, single-trial latencies and frequencies corresponding to the measured ERP/ERD/ERS were obtained by calculating the mean latency and frequency of the selected “top 20%” of points in the time-frequency plane.


Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

Hu L, Zhang ZG, Mouraux A, Iannetti GD - Neuroimage (2015)

Example of a single-trial TFD modelled using both TF-MLR and TF-MLRd.Left panel: A single-trial TFD of a laser-elicited EEG response recorded at electrode Cz. Middle panel: Time-frequency response features (ERP, ERD and ERS) fitted using TF-MLR and TF-MLRd. Note that the information-of-interest (i.e., the ERP, ERD and ERS responses) is preserved, while the information-of-no-interest (e.g., background EEG noise) is largely removed, thus increasing the response SNR. Right panel: The sum of the fitted ERP, ERD and ERS responses constituted the modelled single-trial TFD. TF-MLRd provides a significantly better fit of single trials than TF-MLR (F = 126, p < 0.001).
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401443&req=5

f0025: Example of a single-trial TFD modelled using both TF-MLR and TF-MLRd.Left panel: A single-trial TFD of a laser-elicited EEG response recorded at electrode Cz. Middle panel: Time-frequency response features (ERP, ERD and ERS) fitted using TF-MLR and TF-MLRd. Note that the information-of-interest (i.e., the ERP, ERD and ERS responses) is preserved, while the information-of-no-interest (e.g., background EEG noise) is largely removed, thus increasing the response SNR. Right panel: The sum of the fitted ERP, ERD and ERS responses constituted the modelled single-trial TFD. TF-MLRd provides a significantly better fit of single trials than TF-MLR (F = 126, p < 0.001).
Mentions: Based on the fitted single-trial TFD (Fig. 5, top panel), we calculated, for each TF-feature, the correlation coefficient (CC1, CC2 and CC3 for ERP, ERD and ERS, respectively) between the fitted single-trial TFD and the thresholded TFD obtained from PCA decomposition with Varimax rotation. Single-trial ERP and ERS magnitudes were finally obtained by calculating the mean of the 20% of points (relative to all the non-zero points in the thresholded TFD for each TF-feature [Fig. 1, right panel], the same hereinafter) displaying the highest increase (if CC1 > 0 or CC3 > 0, i.e., a positive fit) or the highest decrease (if CC1 < 0 or CC3 < 0, i.e., a negative fit), respectively. In contrast, single-trial ERD magnitude was obtained by calculating the mean of the 20% of points displaying the highest decrease (if CC2 > 0, i.e., a positive fit), or the highest increase (if CC2 < 0, i.e., a negative fit). Finally, single-trial latencies and frequencies corresponding to the measured ERP/ERD/ERS were obtained by calculating the mean latency and frequency of the selected “top 20%” of points in the time-frequency plane.

Bottom Line: Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations.ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding.This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing, China; Department of Neuroscience, Physiology and Pharmacology, University College London, UK. Electronic address: huli@swu.edu.cn.

No MeSH data available.


Related in: MedlinePlus