<p>I am currently working on the <a href="https://www.kaggle.com/c/grasp-and-lift-eeg-detection/data" rel="nofollow noreferrer">EEG Grasp Dataset</a> from Kaggle. I am aware that I need to filter the EEG signal as it is visually noisy (is there any way to mathematically show it's noisy?). How do I choose what filter I should use from the vast number of types available. Also how do I make sure that the filter improves the SNR while not removing/damaging the information in the EEG signal?</p>

<p>From The kernels in kaggle,
I have seen people use filters like discrete wavelet transform to get the frequency components and filter out unnecessary frequencies or they have used butterworth and notch filters, though one said that the notch filter "distorts" the signal and instead a high pass at 40Hz should be used. People have used different ones so, is there any procedure to determine which filter to use or should I start testing from the most basic filters then get into better frequency domain filters, then spatial filters and finally adaptive filters?</p>

CC BY-SA 4.0
2019-07-09T02:22:55.150Z
What research have you done already? What do you see other people doing? What effort have you made to find an answer on your own? I'm a bit concerned with the questions you've been asking here that you're hoping for StackExchange to be your research mentor, which it can't really be. Your questions are not bad questions, but I'm wary that at your stage of learning if you lean too much on this community you will be completely lost in the future and you aren't actually learning much about how to do research (which includes a ton of self-study).
Bryan Krause
CC BY-SA 4.0
2019-07-09T02:49:39.700Z
@BryanKrause thanks for your advice. Can you help me out by putting me in the right direction by letting me know any books/articles that may be able to help me get on track. Right now, I'm like a person in the middle of an ocean, on a boat with nothing; can you give me a paddle and then I will research and navigate my way through this domain. Thanks!
roaibrain
CC BY-SA 4.0
2019-07-09T15:02:39.743Z
What you are asking about is part of "EEG preprocessing": I think adding the "preprocessing" keyword to your search will help a lot. There are many journal articles where people argue in favor of certain preprocessing steps or others and many distributed toolboxes that have some filtering options built in and include instructions for use. Hopefully those keywords are enough to help you paddle a bit.
Bryan Krause
CC BY-SA 4.0
2019-07-16T21:04:25.530Z
You are basically asking how to be an EEG analyst, which is a large topic. This is a good start: https://erpinfo.org/lecture-slides (Let me know if you have more specific questions on filtering.)
noumenal
<eeg><signal-processing>
How to chose which filter to design for EEG signal?
roaibrain
# How to chose which filter to design for EEG signal?

(Asked by: username_0 on 2019-07-09)

I am currently working on the [EEG Grasp Dataset](https://www.kaggle.com/c/grasp-and-lift-eeg-detection/data) from Kaggle. I am aware that I need to filter the EEG signal as it is visually noisy (is there any way to mathematically show it's noisy?). How do I choose what filter I should use from the vast number of types available. Also how do I make sure that the filter improves the SNR while not removing/damaging the information in the EEG signal?

From The kernels in kaggle,
I have seen people use filters like discrete wavelet transform to get the frequency components and filter out unnecessary frequencies or they have used butterworth and notch filters, though one said that the notch filter "distorts" the signal and instead a high pass at 40Hz should be used. People have used different ones so, is there any procedure to determine which filter to use or should I start testing from the most basic filters then get into better frequency domain filters, then spatial filters and finally adaptive filters?

**Tags:** eeg, signal-processing

> **Comments:**
> **username_1**: What research have you done already? What do you see other people doing? What effort have you made to find an answer on your own? I'm a bit concerned with the questions you've been asking here that you're hoping for StackExchange to be your research mentor, which it can't really be. Your questions are not bad questions, but I'm wary that at your stage of learning if you lean too much on this community you will be completely lost in the future and you aren't actually learning much about how to do research (which includes a ton of self-study).  
> **username_0**: @BryanKrause thanks for your advice. Can you help me out by putting me in the right direction by letting me know any books/articles that may be able to help me get on track. Right now, I'm like a person in the middle of an ocean, on a boat with nothing; can you give me a paddle and then I will research and navigate my way through this domain. Thanks!  
> **username_1**: What you are asking about is part of "EEG preprocessing": I think adding the "preprocessing" keyword to your search will help a lot. There are many journal articles where people argue in favor of certain preprocessing steps or others and many distributed toolboxes that have some filtering options built in and include instructions for use. Hopefully those keywords are enough to help you paddle a bit.  
> **username_2**: You are basically asking how to be an EEG analyst, which is a large topic. This is a good start: https://erpinfo.org/lecture-slides (Let me know if you have more specific questions on filtering.)  

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## 0 answers

