Interesting question. There is indeed some work which focuses on trough to peak transition periods as potentially having informational value, most notably with regard to the N2/P3 complex.

Broadly speaking however, this is not commonly done outside of highly stereotyped ERP component patterns. The tricky bit is that the underlying component structure (i.e. the number, time-course and topography of present components) is unclear. While this can be to some degree empirically assessed via various forms of signal decomposition, the "true answer" remains unknown. As a result, an apparent change in a peak to trough transition zone can be driven by many factors (i.e. an increase in the peak/trough's amplitude or duration or the introduction of a new ERP component etc.) making it difficult to attribute a clear meaning to it.

I would love to hear if someone out there has a different take on this though!

Hi John,

If you are aware of any such articles which talk about the N2/P3 complex, could you please pass it on? it would clarify some questions.

Also, you say that the transition portion can be driven by different factors like increasing peak/trough amplitude and addition of new ERP component. Isnt it the same case with the peaks of the ERPs as well? An extra component is very likely to increase or decrease the amplitude. Or even the latency for that matter.

I always had this thought.. The peaks or troughs indicate the part when the "activity" has reached its maximum and hence, complete.. And that the transition portion indicates the change from one "activity" of the brain to another. However, over time I have sort of realised that it is not necessarily true or correct. again, I have no empirical proof for either supposition.

Dear Anoop

a good question and I mainly agree with the John.

Certainly there is information in the transition state of any ERP. However, to reach the causative factors and assigning a physiological or behavioural changes to the transition needs extensove computational works as well as high resolution recording. To study the transition state we certainly need nonlinear approach to decomposite the multifacet information. Let me gove an example: As you know in an ERP, we focus on the peak and troughs because we can assign respectively amplitude and delay and each of them with one clear factor is conducting speed and population of neuron activated. For transition state the story is different and I think we forst should focus on the underpinning factors contributing this state.

Hello Ali,

That is an important point you made. Convenience in assigning amplitude and latency values!!! It is definitely much easier for peaks and troughs. Which is probably why they are a popular measure. But as you said, we definitely need better statistical and computational approach to see the contributions to the transition portions.

Also, me and my friend actually found that the variance (after bootstrapping) is visibly lesser in the transition portion compared to the actual peaks or trough. So I was actually wondering if the transition portion is the more stable response rather than the peaks

I think nonlinear chaotic approaches can help in this case, although we need a reliable benchmarkto determine the transition extracred measures to physiologic variables. Also it depends on the type of ERP. What type of ERP you are wo on? and on which region?

Honestly I have no idea what these chaotic approaches are.

I'm actually working on auditory erps. Specifically how attention modulates erps in the presence of background noises. My hypothesis is that erps are less variable when you pay attention than when you don't. Incidentally we somehow observed that the transition portion is more stable than the peaks

Ali makes many excellent points on this issue. It is worth noting that he is drawing on the proper definition of transition state in the EEG literature, namely actual critical transition states typically assessed via modeling nonlinear dynamics (e.g. via measures of chaos, e.g. lyapunov exponents) often in epilepsy, seizures and other shifts in conscious mode. This is of course, the correct usage of the term.

I believe Anoop is referring to the time-interval of an ERP waveform that lies in-between a successive maxima (peak) and minima (trough) as a transition period.

Alas, I am not intimately familiar with the nuances of the N2/P3 complex so I'll abstain from recommending readings in that area. I'm certain you'll be able to find what you need with a quick lit search tgh! Or asking an expert on that topic.

It's absolutely true that the same issues that influence transitions influence peaks and troughs directly too. The problem just gets even more complicated when trying to assess changes in the "zones" between them.

The observation of an auditory "transition zone" being more stable (on what metrics if you don't mind me asking?) is an interesting one though. Do let me know when you get a paper out on that. I'd love to check it out.

Dear Anoop

chaotic analysis is for investigating the systems behaviours with chaotic and nonlinearity features and the uncertainty is high.

by the way, regarding auditory ERP, the attention definitely influence the features of ERP but it depends heavily on the regions you are recording and the type of stimulus. The other important factor is the type of attention task you are asking the subjects to perform and also the online or offline type of the attention task. Some specific regions of brain involve in attention and also in auditory processing. as different regions are involved in auditory processing you should take into account the type ot attention, visual attention, auditory attention,... and the region you stimulate and record from. But the stuy is a good one and I would like to know the outcomes

We demonstrated that all the Evoked potentials (either visual, auditory or somestesic) are composite responses. Each component represents the response of a relay station, disposed in time according the delay of the respective response. We did it both by electro-clinical comparisons, and by Mapping investigations, revealing the course of the underlying cerebral influx.

Psatta DM, Matei M: Neural generators of visual evoked potential components. Rev. roum. Neurol. 1997, 35, 13-37.

Psatta DM, Olaru M, Matei M: Visual inflow traveling in the brain evidenced by photic evoked potentials mapping. Rom J Neurol, 2002, 40, 27-44.

Psatta DM, Olaru M, Matei M: Auditory Evoked Potentials Mapping in Source Derivation. Rom J Neurol: 2004, 42, 33-45.

The main factor responsible of the Negative-Positive wave amplitude of these components is the excitation-inhibition ratio. In the case of visual responses the primary N70-P100 response represents the excitation-inhibition ratio in the visual cortex , and has important correlates in visual acuity; (P100 is enhanced by bicucculine, a GABA inhibitor). We showed that N220-P300 represent the hippocampal response (they disappear in case of hippocampal atrophy). In a Go-No Go task, we found the enhancement of P300 in Go trials, and its replacement with a high and large N220 components in No-Go trials.

For more details see:

Psatta, DM. : Electrophysiological Investigation in Brain Diseases, Scholar’s Press, Germany, 2016, 268 pp.