Independent Component Analysis (ICA) | EEG Analysis Example Code

The final video in a 2-part series on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). This video provides a high-level introduction to ICA, compares it to PCA, and walks through some example code.

More in this series:

A resource I found helpful:
- Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw. 2000;13(4–5):411–430. doi:10.1016/s0893–6080(00)00026–5

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Introduction - 0:00
Cocktail Party Problem - 0:32
ICA assumptions - 2:24
PCA vs. ICA - 5:25
Example: Blink Artifact Removal - 7:14
Closing remarks - 12:25

[hanstschohl]

Ive had a class on Neurotech and they did not explain any of this but expected me to write a program using ICA feature selection. I found nothing usefull until this video. thank you so much

[golnazbaghdadi]

The best explanation of ICA that I have ever seen, thanks!

[rlee]

Very clear, thanks! The examples with the mic and blinks were a great inclusion imo.
They made ICA much easier to understand while also displaying the practical application in a fun way :)

[thevitorialima]

Amazing explanation man, I always end up looking your videos for technical but clean explanations!!

[NonstopElectroshock]

Amazing explenation! Thank you so much! You saved my butt for my exam tomorrow

[renanbarella]

Gotcha! it was an amazing explanation about ICA! Thank you very much for that Shawhin! 🙃

[chiragpalan]

thanks for explanation, but may I ask why we want independent components to be non-gaussian?

[raven]

Love it! It was well explained and the example part makes the concept clearer. Thank you : )

[barmawn]

Thank you for your useful contents shawhin🙏🏻

[QifanWang]

THX! Very clear! Could you explain SCA vs PCA? Shared component analysis or Shared variance component analysis?

[martonmunding]

Great explanation, thank you! I was looking for some material to help me with an EEG blink removal problem, so it's the perfect find!

[karenfu]

good examples and codes for beginner like me to learn the concept of ICA!

[reza.partovi]

It was a great video. To restore back the data, shouldn't we add the mean also? After doing the inverse pca tranformation.

[manjukh]

How to apply PCA and ICA for cognitive radio? Spectrum sensing and channel detection.

[sfnembedded]

You have explained it so well, thank you. May I ask? Removing blink or the other noise, Can we do it with some combination such as HPF (High Pass Filter), LPF (Low Pass Filter), and BPF (Band Pass Filter) or there's any special use for ICA or PCA? in case of filtering signal. Thank you!

[Hhushrk]

Hi, what PCA data specifically did you put into ICA? Did you take the variable loadings of each of the 21 PCs? Was that your data frame? I am trying to do this for financial data, would be awesome if you took the output of your PCA video and ran ICA on top of that

[manden_fruta]

Hi Shawhin, thank you for the clear explanation about ICA

I tried your code without any modifications and every time I ran the script, the results are different. Like everytime the resulting signal without blink is different in shape and amplitude, is that normal?
Also, when I plot the original data (64 channels) vs the resulting data_noblink, each channel is not consistent regarding its positions (like data from channel 1 (Data(:, 1)) is now in Data(:, 6) and so on.
My question is, is there a random effect of using ICA that I didn't catch when inverse PCA transform happens? if so, can I use a seed or similar to control this randomness?

[parinazamini]

Thank you so much, this video is really helpful 🌷🌷

[malcolmudeozor]

Hey! Great video! it seeams like your data is still autoscaled after transforming it back to its original form. Would you recommend un-Zscoring the data?

[saadawan]

Hi, Shawhin can you please tell me that how I can made heuristic guess in case of grayscale imges? let say i get PCA_data i.e 10 Principal Components now then how many Independent Components i need, in order to construct gray level co occurance Matrix? while i am doing MR Brain images segmentation and classification in matlab. Thanking you in anticipation.