<p>Latency jitter refers to the phenomenon that an ERP component appears at various time in different trials, without adjusting this difference when averaging across trials will lead to diminished ERP (reducing the peak). Peak-picking (Spencer, Abad &amp; Donchin, 2000) and Woody filter described in Woody (1967) are the ones I can find. Do you know of other methods to address the latency jitter problem?</p>

<p>A graph of latency jitter in ERPs:</p>

<p><a href="https://i.sstatic.net/I1UQa.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/I1UQa.jpg" alt="enter image description here"></a></p>

<ol>
<li>Spencer, K., Abad, E, &amp; Donchin, E. (2000) On the search for the neurophysiological manifestation of recollective experience. Psychophysiology, 37, 494-506</li>
<li>Woody, C. D. (1967). Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals. Medical and biological engineering,5(6), 539-554</li>
</ol>

CC BY-SA 3.0
2016-08-30T11:06:48.467Z
Interesting question, but why would you want to correct for temporal jitter? Nowadays all high-end systems have millisecond precision right?
Robin Kramer-ten Have
CC BY-SA 3.0
2016-08-30T18:54:23.927Z
The ERPs, neural signals generated in the brain occur independently from the system. To correct its latency is in order to have an accurate representation of it when averaging it across trials.
Sophy
CC BY-SA 3.0
2016-08-31T07:08:41.133Z
So am I correct you are talking about physiological latency variability and not about hardware synchronization?
AliceD
CC BY-SA 3.0
2016-08-31T17:32:56.020Z
yes, you are right! it's about physiological latency variability, not about hardware.
Sophy
CC BY-SA 3.0
2016-09-01T08:55:57.743Z
I know that [this paper](https://www.researchgate.net/publication/263286124_A_Comparison_of_Electroencephalography_Signals_Acquired_from_Conventional_and_Mobile_Systems) corrected for (hardware) temporal jitter. Perhaps it may be of use.
Robin Kramer-ten Have
CC BY-SA 3.0
2017-04-02T18:33:23.010Z
I don't have time to write a thorough answer so I will leave a comment. Components like the P3 represent a combination of sources that some to create the observed response. The sources are sometimes termed to be called latent components and they may vary in time course and activation from trial to trial. One method that attempt to separate a signal into source or latent components without regard to temporal jitter is independent component analysis (ICA). ICA will find source waves based on spatial distribution and not care about the timing of the activity in a trial.
NWalk
CC BY-SA 3.0
2017-04-04T00:22:03.683Z
@NWalk Thank you! How about PCA?
Sophy
CC BY-SA 3.0
2017-04-15T00:23:30.517Z
Sorry for the slow response. PCA may work depending on what dimension it is done; however, it makes some assumptions about the source structure that may or may not be true. Additionally pca can not let you pull out individual sources as easily you must always include the preceding components
NWalk
<cognitive-psychology><eeg>
What are other methods for adjusting latency jitter in event-related potentials (ERPs) (P300 in particular) besides Woody filter and peak-picking?
Sophy
# What are other methods for adjusting latency jitter in event-related potentials (ERPs) (P300 in particular) besides Woody filter and peak-picking?

(Asked by: username_0 on 2016-08-30)

Latency jitter refers to the phenomenon that an ERP component appears at various time in different trials, without adjusting this difference when averaging across trials will lead to diminished ERP (reducing the peak). Peak-picking (Spencer, Abad & Donchin, 2000) and Woody filter described in Woody (1967) are the ones I can find. Do you know of other methods to address the latency jitter problem?

A graph of latency jitter in ERPs:

[![enter image description here](https://i.sstatic.net/I1UQa.jpg)](https://i.sstatic.net/I1UQa.jpg)

1. Spencer, K., Abad, E, & Donchin, E. (2000) On the search for the neurophysiological manifestation of recollective experience. Psychophysiology, 37, 494-506
2. Woody, C. D. (1967). Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals. Medical and biological engineering,5(6), 539-554

**Tags:** cognitive-psychology, eeg

> **Comments:**
> **username_1**: Interesting question, but why would you want to correct for temporal jitter? Nowadays all high-end systems have millisecond precision right?  
> **username_0**: The ERPs, neural signals generated in the brain occur independently from the system. To correct its latency is in order to have an accurate representation of it when averaging it across trials.  
> **username_2**: So am I correct you are talking about physiological latency variability and not about hardware synchronization?  
> **username_0**: yes, you are right! it's about physiological latency variability, not about hardware.  
> **username_1**: I know that [this paper](https://www.researchgate.net/publication/263286124_A_Comparison_of_Electroencephalography_Signals_Acquired_from_Conventional_and_Mobile_Systems) corrected for (hardware) temporal jitter. Perhaps it may be of use.  
> **username_3**: I don't have time to write a thorough answer so I will leave a comment. Components like the P3 represent a combination of sources that some to create the observed response. The sources are sometimes termed to be called latent components and they may vary in time course and activation from trial to trial. One method that attempt to separate a signal into source or latent components without regard to temporal jitter is independent component analysis (ICA). ICA will find source waves based on spatial distribution and not care about the timing of the activity in a trial.  
> **username_0**: @username_3 Thank you! How about PCA?  
> **username_3**: Sorry for the slow response. PCA may work depending on what dimension it is done; however, it makes some assumptions about the source structure that may or may not be true. Additionally pca can not let you pull out individual sources as easily you must always include the preceding components  

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## 0 answers

