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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

Generation of regressors in the TF-MLR approach.Left panel: Time-frequency representation of one of the thresholded features (‘ERP’, in this example) of the EEG response, obtained by PCA decomposition with Varimax rotation (Fig. 1). Right Panel: The three regressors obtained by the TF-MLR approach represent the average (Gaussian smoothing; the spread parameters of the Gaussian kernel are: σx = 30 ms, σy = 3 Hz), the temporal derivative and the frequency derivative of the ERP response, respectively. The temporal and frequency derivatives will be used to capture the variability in latency and frequency of single-trial TFDs.
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f0015: Generation of regressors in the TF-MLR approach.Left panel: Time-frequency representation of one of the thresholded features (‘ERP’, in this example) of the EEG response, obtained by PCA decomposition with Varimax rotation (Fig. 1). Right Panel: The three regressors obtained by the TF-MLR approach represent the average (Gaussian smoothing; the spread parameters of the Gaussian kernel are: σx = 30 ms, σy = 3 Hz), the temporal derivative and the frequency derivative of the ERP response, respectively. The temporal and frequency derivatives will be used to capture the variability in latency and frequency of single-trial TFDs.

Mentions: Using the Taylor expansion, the MLR model can be simplified as:(3)Ftf≈k1F1tf+k1a1∂F1tf∂t+k1b1∂F1tf∂f+k2F2tf+k2a2∂F2tf∂t+k2b2∂F2tf∂f+k3F3tf+k3a3∂F3tf∂t+k3b3∂F3tf∂fwhere , , and are the temporal derivatives of ERP, ERD, and ERS; and , , and are the frequency derivatives of ERP, ERD, and ERS respectively. Thus, a single-trial TFD can be approximated as the sum of a set of weighted basis (average, its temporal derivative and its frequency derivative) (Fig. 3).


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

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

Generation of regressors in the TF-MLR approach.Left panel: Time-frequency representation of one of the thresholded features (‘ERP’, in this example) of the EEG response, obtained by PCA decomposition with Varimax rotation (Fig. 1). Right Panel: The three regressors obtained by the TF-MLR approach represent the average (Gaussian smoothing; the spread parameters of the Gaussian kernel are: σx = 30 ms, σy = 3 Hz), the temporal derivative and the frequency derivative of the ERP response, respectively. The temporal and frequency derivatives will be used to capture the variability in latency and frequency of single-trial TFDs.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

f0015: Generation of regressors in the TF-MLR approach.Left panel: Time-frequency representation of one of the thresholded features (‘ERP’, in this example) of the EEG response, obtained by PCA decomposition with Varimax rotation (Fig. 1). Right Panel: The three regressors obtained by the TF-MLR approach represent the average (Gaussian smoothing; the spread parameters of the Gaussian kernel are: σx = 30 ms, σy = 3 Hz), the temporal derivative and the frequency derivative of the ERP response, respectively. The temporal and frequency derivatives will be used to capture the variability in latency and frequency of single-trial TFDs.
Mentions: Using the Taylor expansion, the MLR model can be simplified as:(3)Ftf≈k1F1tf+k1a1∂F1tf∂t+k1b1∂F1tf∂f+k2F2tf+k2a2∂F2tf∂t+k2b2∂F2tf∂f+k3F3tf+k3a3∂F3tf∂t+k3b3∂F3tf∂fwhere , , and are the temporal derivatives of ERP, ERD, and ERS; and , , and are the frequency derivatives of ERP, ERD, and ERS respectively. Thus, a single-trial TFD can be approximated as the sum of a set of weighted basis (average, its temporal derivative and its frequency derivative) (Fig. 3).

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