[Eeglablist] high frequency oscillation- eeg advice

Thu Jan 26 14:12:53 PST 2017

Dear Makoto,
I actually switched to the pop eeg filt eeglab function and it now Really attenuated anything >40Hz, and my ERPs are cleaner. However I filtering between 0.1-40Hz at the same time in the GUI (I interned the high pass and low pass simultaneously). Is this OK to do? The frequency response looks OK.

The filter order was automatically set very high by the GUI, but it's continuous data and I have room without events at the beginning and end of the data so any edge effects can be disgusted. What do you think?

Also, this is for my ERP analysis- I trained ICA on a 1Hz high pass filtered set.
I won't be using granger causality but I will be estimating phase during ITC.
Best wishes, and thanks,
Jumana

------------------------------------------
Jumana Ahmad
Post-Doctoral Research Worker in Cognitive Neuroscience
EU-AIMS Longitudinal European Autism Project (LEAP) & SynaG Study
Room M1.26.Department of Forensic and Neurodevelopmental Sciences (PO 23) | Institute of Psychiatry, Psychology & Neuroscience | King’s College London | 16 De Crespigny Park | London SE5 8AF

Phone: 0207 848 5359| Email: jumana.ahmad at kcl.ac.uk | Website: www.eu-aims.eu | Facebook: www.facebook.com/euaims

________________________________
From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Sent: 26 January 2017 23:49:50
To: Ahmad, Jumana
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] high frequency oscillation- eeg advice

Dear Jumana,

It's a bad idea to perform ICA with 0.1Hz high-pass filtered data. The cutoff frequency is too low. See this page and the referenced paper.

https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline#High-pass_filter_the_data_at_1-Hz_.28for_ICA.2C_ASR.2C_and_CleanLine.29<https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsccn.ucsd.edu%2Fwiki%2FMakoto%2527s_preprocessing_pipeline%23High-pass_filter_the_data_at_1-Hz_.28for_ICA.2C_ASR.2C_and_CleanLine.29&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C35190d19320946ffd52408d446355d20%7C8370cf1416f34c16b83c724071654356%7C0&sdata=XQPtEZ22iFzlB2PVii2GjBWVdfPOZZnmnmOfGa8CbgI%3D&reserved=0>

> A 30Hz low pass does not help to get rid of the oscillation, which is really significant in the data.

Check the channel frequency spectra and tell me if you see peaks in it. If necessary, you can cut it off using a designed low-pass filter (not like Butterworth...)

> I use a butterworth filter, which is good for ERP analysis with low phase distortion.

Do not make qualitative judgement just because something is NOT a classic Butterworth. Of course, if the attenuation is small, the phase 'distortion' is small. But if such small attenuation is not useful, it does not help at all! Also, be careful with the word 'phase'. Particularly people who do not know basics of signal processing believe phase as some magical thing. If you are not performing Granger Causality Analysis or something, you don't need to be so worried about phase issue in practice.

> I also already run ICA, but in some datasets there is a very significant high frequency oscillation.

Remember, to eliminate this is more important than being afraid of qualitative phase issue.

> However, I can see the high frequency oscillations in my ERP, which is not ideal and now I need to try and get rid of it further.
Can I filter again on top of the data which already has already undergone ICA- I only use ICA to remove blinks?

You'd better to filter the data on continuous state. If you need to filter the epoched data, the half of filter length from both ends becomes unreliable.

> Should I do cleanline, although it would have to be after ICA now- I read this is not advisable.
> Should I use a notch filter?

If you see > 20dB line noise, Cleanline may not help. In this case, I would simply apply a designed low-pass filter, either Hamming (-50dB) or Blackman (-70dB) using firfilt(). See 'Tools' -> 'Filter the data' -> 'Windowed sinc FIR filter'.

There are different guys saying different things about data preprocessing. It is confusing, I know! The only good solution for this is to become an engineer yourself...

Makoto

On Tue, Jan 24, 2017 at 7:00 AM, Ahmad, Jumana <jumana.ahmad at kcl.ac.uk<mailto:jumana.ahmad at kcl.ac.uk>> wrote:
Hi  Everyone,
I am running a large scale ERP analysis. I filtered 1-40Hz (ICA AMICA), or 0.1-40Hz for the ERP dataset. A 30Hz low pass does not help to get rid of the oscillation, which is really significant in the data. I use a butterworth filter, which is good for ERP analysis with low phase distortion.
I also already run ICA, but in some datasets there is a very significant high frequency oscillation.
I do not use cleanline, which is not typical in the literature I have been basing my pipeline on.

However, I can see the high frequency oscillations in my ERP, which is not ideal and now I need to try and get rid of it further.
Can I filter again on top of the data which already has already undergone ICA- I only use ICA to remove blinks?
Should I do cleanline, although it would have to be after ICA now- I read this is not advisable.
Should I use a notch filter?

Any help would be appreciated.
Best wishes,
Jumana

------------------------------------------
Jumana Ahmad
Post-Doctoral Research Worker in Cognitive Neuroscience
EU-AIMS Longitudinal European Autism Project (LEAP) & SynaG Study
Room M1.09. Department of Forensic and Neurodevelopmental Sciences (PO 23) | Institute of Psychiatry, Psychology & Neuroscience | King’s College London | 16 De Crespigny Park | London SE5 8AF

Phone: 0207848 0260| Email: jumana.ahmad at kcl.ac.uk<mailto:antonia.sanjose at kcl.ac.uk> | Website: www.eu-aims.eu<https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.eu-aims.eu%2F&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C35190d19320946ffd52408d446355d20%7C8370cf1416f34c16b83c724071654356%7C0&sdata=vVSbBawKS2L6e9vVZXkIMrj%2B%2FwqhrreJRZRNr6zUN2s%3D&reserved=0> | Facebook: www.facebook.com/euaims<https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.facebook.com%2Feuaims&data=01%7C01%7Cjumana.ahmad%40kcl.ac.uk%7C35190d19320946ffd52408d446355d20%7C8370cf1416f34c16b83c724071654356%7C0&sdata=pOPzKLDwxDuoGPJKPTlPGwurARTFmxrwy2VEjUlBtP8%3D&reserved=0>

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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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