Convolution in the time domain

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Күн бұрын

This video lesson is part of a complete course on neuroscience time series analyses.
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Пікірлер: 33

@romanvereb7144 4 жыл бұрын

Mike X Cohen - the unsung hero of our age

@mikexcohen1 4 жыл бұрын

Aww, now you make me blush ;)

@weilawei 4 жыл бұрын

Super clear explanation, very intuitive. Thank you.

@mikexcohen1 4 жыл бұрын

You're welcome!

@IamGQ87 4 жыл бұрын

really very pedagogical. Thank you

@helenzhou3530 3 жыл бұрын

This video is super helpful, thank you so much!

@tranez2205 3 жыл бұрын

Awesome video! Thank you so much!

@violincrafter 4 жыл бұрын

Wings of convolution: a good band name

@mikexcohen1 4 жыл бұрын

I'll be the back-up kazoo player.

@jaimelima2420 2 жыл бұрын

This is good stuff. Good Job!

@ormedanim 3 жыл бұрын

you lost me at God's perspective, now I'm flipping (out) instead of the kernel :D But I am very thankful for all the videos and the ANTS book

@mikexcohen1 3 жыл бұрын

Nice.

@jaimelima2420 2 жыл бұрын

Richard Hamming's Digital Filter explains this god's perspective in a different way, worth checking imho.

@kaymengjialyu5086 3 жыл бұрын

You are such a good teacher :)

@mikexcohen1 3 жыл бұрын

aww, thanks!

@sachindrad.a836 2 жыл бұрын

Very nice explanation

@RenanAlvess 3 жыл бұрын

congratulations for explanation, was very enlightening for me

@mikexcohen1 3 жыл бұрын

Nice to hear. I made this video just for you, Renan :D

@bokkieyeung504 3 жыл бұрын

I'm wondering why not aligning the center of the kernel with the edge of the signal (still need zero-padding, but less extra zeros) so that we can get the result with exact same length as the original signal, thus no need to cut off the "wings"?

@mikexcohen1 3 жыл бұрын

If you are implementing convolution manually in the time domain using for-loops, then yes, that's convenient. But the formal procedure is done to match the implementation in the frequency domain, which is much faster.

@williammartin4416 Жыл бұрын

Excellent explanations

@mikexcohen1 Жыл бұрын

Glad you liked it!

@hurstcycles 3 жыл бұрын

If the kernel is a morlet wavelet (formed by combining a constant sine wave and gaussian) and symetrical around the mid point, flipping the kernel is not necessary, is that accurate? Thanks for the great video

@mikexcohen1 3 жыл бұрын

Kindof, but be careful with the descriptions: The kernel always needs to be flipped, but if the kernel is symmetric, then flipping has no effect. (Also, sine is an odd function and thus is asymmetric; cosine is symmetric about zero.)

@jesusdanielolivaresfiguero4752 3 жыл бұрын

Is there a way to buy your Analyzing Neural Time Series Data book on credit for monthly payments?

@mikexcohen1 3 жыл бұрын

Hi Jesus. Find my email address (it's on my CV) and send me an email about this.

@MrPabloguida Жыл бұрын

Is it fair to say that the result signal, even after cutting out the wings, will still be "contaminated" by the zero padding for at least another half kernel length, which would be when it start having a pure and clean signal/kernel convolution? Does it make sense?

@mikexcohen1 Жыл бұрын

It is certainly the case that edges are always difficult to interpret from any kind of filtering. When possible, it's best to have extra time series before and after the period of interest, so that you can ignore those edges.

@brixomatic Жыл бұрын

Wouldn't the convolution it be a better representation of the signal, if you could wrap around the edges of the signal? I.e. you'd start the kernel's mid point at the start of the signal and take the left half of the kernel from the right side of the signal and if the kernel exceeds the right bounds, take the data from the start of the signal? This way your convolution would have the same length as the signal, but operate only on the signal's data and not sneak in zeroes that have no meaning and pollute the results.

@mikexcohen1 Жыл бұрын

Yes, that's called "circular convolution"; what I explain here is "linear convolution." Both methods produce edge effects that should not be interpreted.

@user-vo7oe1be8j 8 ай бұрын

@@mikexcohen1 teacher. I want to make sure if if my thoughts are correct. The edge effect will occur when we use 'Convolution Theory' to obtain the result of the convolution between two signals. This is because 'Convolution Theory' uses FFT. If the max frequency of the two signals exceeds the Nyquist Frequency, aliasing will occur. This is why it's called the "edge effect", right? Sorry I'm not native English speaker, if something's confusing, please correct me.