[Eeglablist] ICA in short duration recordings

[Beach, Paul]

Scott,

Thanks for responding.

It’s 64 channel EEG. If my ’time points per epoch’ is the same as ’number of time samples’, then after downsampling this result is ~47,000 for a typical subject with ~180s of recording. For 360s recording ’time points per epoch’ is ~84,000.

So if I’m interpreting things correctly
3min recording: k ~ 11
5min recording: k ~ 20

If I’m interpreting Makoto’s description of this question correctly ("minimum of (number of channel)^2 x 20 to 30 data points to perform ICA”), I theoretically need that ‘k’ to be between 20-30. So it seems as though a minimum of 5 min or so would be necessary for ICA, which I think I’ve seen others describe.

If that’s the case, better to go off just ASR, I suppose, given most subjects have about 3min of recording, correct?




Paul Beach D.O., Ph.D.
Movement Disorders Fellow, PGY6
Emory University School of Medicine

On Jan 20, 2022, at 11:58 AM, Scott Makeig <smakeig at gmail.com<mailto:smakeig at gmail.com>> wrote:


Paul -

You omit a crucial variable ... how many channels are you decomposing? The complexity of the ICA model of the data is proportional to the square of the number of channels, since ICA learns a square (channels x channels) unmixing matrix. As the data length becomes shorter, the number of channels that can be well-decomposed becomes smaller.  What is k for you? where

k = number_of_time_samples / number_of_channels^2

Scott

On Thu, Jan 20, 2022 at 11:54 AM Beach, Paul via eeglablist <eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>> wrote:
EEGLAB team – and other EEG’ers,

I am currently working with a number of datasets with relatively short durations – 3-5 minutes, generally. My goal is to examine endogenous ERPs from heartbeat processing (heartbeat evoked potential) in controls and a clinical group. I utilize a processing protocol whose artifact rejection mainly involves ASR, epoching to heartbeats, and then use of ICA to reject components at the single subject level.

I almost always run into an issue whereby I only get a few components in ICA (usually out of 50-60) that are *clearly* neural in origin and maybe a few where it’s a more difficult call. My understanding is that, in general, the majority of components will not be necessary to keep/won’t be neural (particularly ICs after the first 20 or so). However, talking with other EEG’ers I find that they will typically have 10 or so.

Notwithstanding that EEGLAB does recommend not rejecting components until group analysis, I wanted to query thoughts on the ‘appropriateness’ of even using ICA on recordings of such relatively short duration. I’ve read the brief discussion in Makoto’s processing page on this and ASR does a great job removing lots of things like eye blinks and even clear cardiac field artifact

SO: should one just rely on ASR and perhaps use ICA for eye components that are missed (and keep everything else)? Should one avoid ICA completely for shorter duration recordings? Should I continue what I’m already doing? Some other middle ground? Or should I just forget about single subject level IC rejection completely and rely on its use in group-level analysis?

Thanks for your time and thoughts.
--
Paul Beach DO, PhD
PGY6, Movement Disorder Fellow
Department of Neurology
Emory University School of Medicine


_______________________________________________
Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html<https://urldefense.proofpoint.com/v2/url?u=https-3A__nam11.safelinks.protection.outlook.com_-3Furl-3Dhttp-253A-252F-252Fsccn.ucsd.edu-252Feeglab-252Feeglabmail.html-26data-3D04-257C01-257Cpaul.anthony.beach-2540emory.edu-257C180f439303e6499882f408d9dc360bb4-257Ce004fb9cb0a4424fbcd0322606d5df38-257C0-257C0-257C637782946950139475-257CUnknown-257CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0-253D-257C3000-26sdata-3DnhsKmSkwX8I3OMux-252B-252FFgBK9ul-252F5LjaJwoGV-252Fa2gdgXk-253D-26reserved-3D0&d=DwIGaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=XpXliGjlp1N2XuX29QMSL2xTML-7Q9AGQGyx_ptImzUNtb_LvrwkMitduE12lef1&s=1FIGm_HkS9J6hVcH0xxk3v32OqlHNu8UnpcDPzLyy9U&e= >
To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu<mailto:eeglablist-unsubscribe at sccn.ucsd.edu>
For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu<mailto:eeglablist-request at sccn.ucsd.edu>


--
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<https://urldefense.proofpoint.com/v2/url?u=https-3A__nam11.safelinks.protection.outlook.com_-3Furl-3Dhttp-253A-252F-252Fsccn.ucsd.edu-252F-7Escott-26data-3D04-257C01-257Cpaul.anthony.beach-2540emory.edu-257C180f439303e6499882f408d9dc360bb4-257Ce004fb9cb0a4424fbcd0322606d5df38-257C0-257C0-257C637782946950295709-257CUnknown-257CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0-253D-257C3000-26sdata-3D3zxo2Gvgnndy7V47VaF9sn-252B7K7hbRMlIhbj9ElOeNHQ-253D-26reserved-3D0&d=DwIGaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=XpXliGjlp1N2XuX29QMSL2xTML-7Q9AGQGyx_ptImzUNtb_LvrwkMitduE12lef1&s=1ScTe7kPpjQbA-BiItqnxb84fVCks4m3nei9OO44L1Q&e= >