[Eeglablist] interpolating boundary events

[Gin Estrella Cruz]

Hi Scott,

Thank you so much for the detailed explanation! I really appreciate the
clarification on how EEGLAB handles boundary events and the importance of
not confusing ICA decomposition with artificially interpolated data. Your
point about ensuring the subject remains in a stable cognitive state for
ICA training is also very helpful—I’ll keep that in mind for my
resting‐state fusion analyses.

I’m interested in exploring AMICA as you suggested, particularly for cases
where the participant’s state might shift (like drowsiness in a longer
resting‐state run). I’ll also look into the EEG.urevent structure.

Thanks again for all your work on EEGLAB and for these pointers. I’ll
follow up if I have more questions once I dive into AMICA and the urevent
details.

Best regards,

Gin

On Sun, Mar 9, 2025 at 11:35 PM Scott Makeig <smakeig at gmail.com> wrote:

> Gin -
>
> Yes, in constructing EEGLAB we were careful to mark boundary events, and
> to have core routines (e.g. filtering) respect them (by not filtering
> through the jagged discontinuity each can create).
>
> ICA decomposition, in particular, pays no attention to the time ordering
> of the data. In fact, it randomly shuffles their order before each training
> iteration (while, of course, not affecting the original order of the data
> points in the EEG dataset). While it is advantageous to decompose as much
> data as possible, this data should have been while the participant remained
> in the same condition or cognitive state - and should *not* include
> interpolated data, which will tend to 'confuse' the decomposition.
>
> The question as to the consistency of the cognitive state of the subject
> during the recording is an important one. Generally, if the participant
> performs the same task throughout the ICA training data, with generally
> similar performance, then we can imagine their cognitive state to have
> remained largely the same. If you want to investigate this - or want to
> decompose data in which cognitive state is not or may not be maintained
> (sleep, drowsy performance, different tasks, eyes open vs. eyes closed,
> etc.), EEGLAB includes a powerful ICA algorithm for assessing this - AMICA.
> See papers by Shawn Hsu (+ Makeig) for examples of the power of AMICA to
> discern changes in cognitive state.
>
> Also, you might investigate the *EEG.urevent* structure, which retains
> markers of all recorded event onsets in the original (*ur*) recording.
>
> Scott
>
> On Sat, Mar 8, 2025 at 6:42 PM Gin Estrella Cruz <
> g.estrella.cruz at gmail.com> wrote:
>
>> Hi Scott,
>>
>> I initially thought interpolating short boundary events was needed to
>> maintain a strictly continuous dataset for my subsequent analyses like ICA
>> or PSD. But I’ve since found out that EEGLAB typically skips or removes
>> those intervals automatically, so interpolation is often unnecessary. I
>> tried cubic spline interpolation across the short gaps and I got flat
>> lines, so bridging gaps with artificial data would seem to distort
>> subsequent analyses. Therefore, I no longer plan to interpolate short
>> boundary events unless I’m running a method that explicitly requires a
>> single unbroken time series (like nonlinear time series methods), but even
>> then, I may just split the data into segments, removing the boundary gaps.
>> By the way, I am trying to do a fusion of EEG with resting-state fMRI.
>>
>> And thanks to EEGLAB and its developers -- such a wonderful tool!
>>
>> -Gin
>>
>> On Sun, Mar 9, 2025 at 3:07 AM Scott Makeig <smakeig at gmail.com> wrote:
>>
>>> Gin -
>>>
>>> Why would you want to do that?
>>>
>>> Scott Makeig
>>>
>>> On Wed, Mar 5, 2025 at 8:39 PM Gin Estrella Cruz via eeglablist <
>>> eeglablist at sccn.ucsd.edu> wrote:
>>>
>>>> Hi everyone,
>>>>
>>>> What is the current consensus on interpolating across boundary events?
>>>> Which algorithm or EEGLAB plug-in would you recommend?  I plan to
>>>> interpolate across a threshold of no longer than 2-seconds of boundary
>>>> event interval.
>>>>
>>>> I'd appreciate your help.
>>>>
>>>> Thanks,
>>>> Gin Cruz
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>>>
>>>
>>> --
>>> Scott Makeig, Research Scientist and Director, Swartz Center for
>>> Computational Neuroscience, Institute for Neural Computation, University of
>>> California San Diego, La Jolla CA 92093-0559,
>>> http://sccn.ucsd.edu/~scott 
>>>
>>
>
> --
> Scott Makeig, Research Scientist and Director, Swartz Center for
> Computational Neuroscience, Institute for Neural Computation, University of
> California San Diego, La Jolla CA 92093-0559, http://sccn.ucsd.edu/~scott 
>