[Eeglablist] EEG 'localizer'?

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Fri Dec 26 13:22:33 PST 2014

> Hello Johan,
> Hoping all is well and have a good new year!
> My strongest recommendation is that you do plenty of pilot testing from
> short & long versions of tasks, and show yourself that you can generate
> robust and intended metrics from the mini-tasks.
> ​Some other thoughts are listed
>  below that you might find useful to consider.
> Good luck with your efforts, it will be interesting to hear more
> ​about ​
> them someday.
> Best wishes, Tarik
> ************************
> EEG fingerprinting and localizer tasks are of interest to the general
> community for multiple reasons, but I don't think there're are yet
> candidates for batteries of valid "EEG mini-tasks". Your question is
> tricky, as ERP and EEG tasks/metrics are well-established, but EEG sources
> and "localizer" tasks are less well established. Overall, a lot of
> reliability, validity, and single-trial work still  needs to be done across
> the EEG/ERP/source-estimation field before we can have a good advanced
> mini-batteries of EEG tasks to deploy. This topic also connects with the
> search for quickly-generated neurocognitive markers for psychiatric,
> neurological, BCI, and operative monitoring endeavors. Exploring recent
> literature and reviews in that area would highlight some major candidate
> markers (ie, CENTRICS efforts). Some EEG companies sell standardized
> ERP/EEG tasks/metrics with their systems, and there is of course a broad
> market in non-EEG cognitive-testing batteries (e.g., NIH Toolbox, Cantab,
> etc.). Overall, responses to your question about whether it's safe to use a
> task are highly dependent on the evidence base considered appropriate by
> the respondent (e.g., # and quality of previous studies, consistency of
> patterns, constraints on metrics, psychometric properties, external
> validity, etc..).
> Of some help might be the several reviews of basic guidelines for
> ​major ERPs such as ​
> P300, MMN, ERN, FRN, N400
> ​, including
>  information about minimal recommended trial count and other constraints
> that are essential. You can also find articles that have tried to establish
> minimum-trial-numbers for the ERN, FRN, and MMN, often in pediatric
> populations.  All these can be found on Google Scholar, most within last ~7
> years. You may also want to search for other terms such as EEG +
> fingerprinting, cognitive state monitoring,  and localizer.
> There are some trade-offs that require consideration here in terms of
> fidelity (more trials, less tasks) vs. bandwidth (less trials, more tasks),
> in terms of the EEG metrics/constructs to be derived (established ERP/EEG
> metrics vs. simple "localizers" or "fingerprints"), and in terms of how you
> expect to use the resulting EEG-derived metrics (eg., diagnostics,
> exploratory analyses, tracking ICs from the min-battery in other
> conditions). If you want to connect with known ERPs, you will need some
> minimim number of valid trials for each ERP metrics to be able to compare
> to previous findings. The more you deviate from previous protocols, the
> more likely that the resultant metrics will be tentative/exploratory. See
> Luck's MONSTER method as an interesting approach in the ERP area. In your
> search for an EEG-localizer battery, a good rule of thumb cutoff is around
> 50 trials per each kind of tasks (50 face trials, 50 sound trials, 50
> finger-tap trials) - or 50 trials per condition within each task. You might
> cut things to 30 trials if you have few artifacts and can denoise the data
> accurately. If you're not looking for any particular well-established ERP,
> you could essentially just present stimuli in different modalities for ~50
> trials, mostly or all with passive presentation requiring no response, or a
> simple response to maintain attention to the stimuli. If you'd like
> involvement of executive processes, then you could vary the stimuli to
> include oddballs or responses. For a very brief task generating a
> ​n ERN or FRN
> ,
> ​ consider ​
> experimenting
> ​with ​
> ​protocols which require
>  a speeded-response-period
> ​ and/or false negative feedback
> , so as to maximize
> ​ the number of trials generating fronto-medial negativities.​
> If you want to get ICA maps of visual, auditory, or other networks, then
> you might be able to simply show a visual stimulus such as a check board or
> faces, for example, to get good visual Independent Components (though the
> latter stimuli would activate higher-order visual regions). Same for
> auditory and sensori-motor networks. There are a good number of tasks that
> give reliable activation within and/or across specific neuropsychological
> systems. Further, many research groups have decomposed brief periods of
> resting data, and derived Independent components associated with known
>  intrinsic brain networks. This suggests rest tasks can also work to get
> "localizers
> ​"​
> or
> ​"​
> fingerprints"
> ​.

> ********************
> On Tue, Dec 23, 2014 at 10:53 AM, Johan <johanvandermeer at gmail.com> wrote:
>> Dear all,
>> I am searching for a single task, which can (well.. ideally..) be
>> performed in under about 5-10 minutes (no longer), and which can get me
>> (ideally!) an ERP of:
>> visual
>> auditory
>> motor
>> go-nogo (response inhibition)
>> ... and, if possible, also the possibility to impart specific frequencies
>> in the EEG signal that can be extracted later on - sort of like the case
>> when you have a flickering checkerboard with pattern reversals every 0.2
>> seconds for a 5 Hz 'base' signal in the EEG.
>> The objective is to compress as much information as possible into a
>> single EEG task - so that you can compare the outcome (within subject
>> analysis) between 2 different situations... without (1) totally confusing
>> the subject, and (2) still have the time to do a larger experiment.
>> So - it's sort of like an EEG 'localizer' task.
>> Does anyone ever try this before, or has seen anywhere something like
>> this being attempted? Or rather you think this is just impossible and not
>> worth the effort? :-)
>> Thank you very much for your time and kind regards,
>> Johan van der Meer
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