How to deal with 1/f noise in low frequency oscillations?

On Nov 15, 5:46&#4294967295;am, Gangadhar <gange...@gmail.com> wrote:
> Dear all,
>
> Here are my clarifications:
>
> 1. Acquisition : I can not avoid 1/f noise, as the noise is basically
> originating from the brain and I work with non-invasive methods (EEG)
> acquire the signals. I believe, it is the same even if one goes
> invasive. The signals and noise have same origins. This is quite
> common with biological signals. Also, all the other noise ( eye
> movement, or muscular etc) is (and will be) avoided as much as
> possible during the experiment.
>
> 2. Multiple measurement : Yes, indeed I have many electrodes recoding
> simultaneously across different spatial locations. Eventually, spatial
> filtering does help increase SNR. But this is not enough to remove 1/f
> noise in the low frequency range. And I can not afford to do multiple
> measurements in time, as per the experimental needs.
>
> 3.Real-time: I would like to detect within 2s with respect to a visual-
> stimuli which results in fluctuations in the [0.2 0.8] Hz with respect
> to cognition of the subject.
>
> 4. Using Choppers : This may help do amplitude modulation of my
> signals to a chopper-frequency (or carrier frequency, say 200Hz) where
> IIR filter design is easier. But, designing of filter with sharper
> transition between 200.1Hz and 200.2Hz is still a problem!
>
> Looking forward for more suggestions! :-)
>
> Thanks a lot for your attention!
> G.
>
> On Nov 6, 4:52&#4294967295;pm, Tim Wescott <t...@seemywebsite.com> wrote:
>
> > On 11/05/2010 01:46 PM, glen herrmannsfeldt wrote:
>
> > > mnentwig<markus.nentwig@n_o_s_p_a_m.nokia.com> &#4294967295;wrote:
>
> > >> One more thought (see "averaging" above):
> > >> Can you get multiple measurements where signal and noise aren't fully
> > >> correlated? There are many tricks to achieve this, for example using
> > >> multiple sensors or chopping the signal before the noise source.
>
> > > I was assuming that the noise was from the source. &#4294967295;Consider
> > > thermal noise from a resistor. &#4294967295;One can cool it down to reduce
> > > it, but otherwise there isn't much else that can be done.
> > > One can't cool the brain down to reduce its low frequency noise.
>
> > My understanding of EKGs (mostly garnered from this group) is that
> > there's a lot of noise from voluntary muscles and other localized
> > sources -- so there's often physical things you can do to reduce noise,
> > before you get down to swallowing the bitter pill of a one dimensional
> > vector of signal+noise that you have to sort out somehow.
>
> > One hopes that EEG is the same.
>
> > --
>
> > Tim Wescott
> > Wescott Design Serviceshttp://www.wescottdesign.com
>
> > Do you need to implement control loops in software?
> > "Applied Control Theory for Embedded Systems" was written for you.
> > See details athttp://www.wescottdesign.com/actfes/actfes.html

This sounds similar to the "Baseline Wander" problem. Have you
searched on that term?

John

Thanks John! I am looking now at some papers based on this term! :-)

> This sounds similar to the "Baseline Wander" problem. Have you
> searched on that term?
>
> John

On Nov 15, 5:46&#4294967295;am, Gangadhar <gange...@gmail.com> wrote:
> Dear all,
>
> Here are my clarifications:
>
> 1. Acquisition : I can not avoid 1/f noise, as the noise is basically
> originating from the brain and I work with non-invasive methods (EEG)
> acquire the signals. I believe, it is the same even if one goes
> invasive. The signals and noise have same origins. This is quite
> common with biological signals. Also, all the other noise ( eye
> movement, or muscular etc) is (and will be) avoided as much as
> possible during the experiment.
>
> 2. Multiple measurement : Yes, indeed I have many electrodes recoding
> simultaneously across different spatial locations. Eventually, spatial
> filtering does help increase SNR. But this is not enough to remove 1/f
> noise in the low frequency range. And I can not afford to do multiple
> measurements in time, as per the experimental needs.
>
> 3.Real-time: I would like to detect within 2s with respect to a visual-
> stimuli which results in fluctuations in the [0.2 0.8] Hz with respect
> to cognition of the subject.
>
> 4. Using Choppers : This may help do amplitude modulation of my
> signals to a chopper-frequency (or carrier frequency, say 200Hz) where
> IIR filter design is easier. But, designing of filter with sharper
> transition between 200.1Hz and 200.2Hz is still a problem!
>
> Looking forward for more suggestions! :-)
>
> Thanks a lot for your attention!
> G.

Maybe I am missing something, but how can you hope to isolate a change
in a 0.2 Hz signal within a 2 second delay?  The wave period is 5
seconds and 2 seconds is less than half that.  This sounds pretty
tricky to me... but I'm not an expert at this.

When you refer to a transition band between 200.1 and 200.2 that is
only 0.1 Hz which equates to a 10 second period.  There are
limitations to what you can do with only 2 seconds of data.

Rick

as said earlier, the time-bandwidth product is a fundamental limitation. 
Chopping is useless if you chop *both* the signal and the noise: The
filtering itself won't get any less impossible at a different frequency. 

rickman <gnuarm@gmail.com> wrote:
> On Nov 15, 5:46&#4294967295;am, Gangadhar <gange...@gmail.com> wrote:
(snip)

>> 3.Real-time: I would like to detect within 2s with respect to a visual-
>> stimuli which results in fluctuations in the [0.2 0.8] Hz with respect
>> to cognition of the subject.
(snip)

> Maybe I am missing something, but how can you hope to isolate a change
> in a 0.2 Hz signal within a 2 second delay?  The wave period is 5
> seconds and 2 seconds is less than half that.  This sounds pretty
> tricky to me... but I'm not an expert at this.

Isn't it the bandwidth, and not the frequency, that is important?
If it is 0.2 to 0.8Hz then the bandwidth is 0.6Hz, which you might
be able to do with 2s, though that is pretty close.

So many years ago, when I would try to adjust the filters on
a lock-in amplifier, such that the signal would come through,
but not noise, and fast enough to be useful.

2 seconds will be pretty hard, I agree.

> When you refer to a transition band between 200.1 and 200.2 that is
> only 0.1 Hz which equates to a 10 second period.  There are
> limitations to what you can do with only 2 seconds of data.

-- glen

On Nov 19, 3:25&#4294967295;pm, glen herrmannsfeldt <g...@ugcs.caltech.edu> wrote:
> rickman <gnu...@gmail.com> wrote:
> > On Nov 15, 5:46 am, Gangadhar <gange...@gmail.com> wrote:
>
> (snip)
>
> >> 3.Real-time: I would like to detect within 2s with respect to a visual-
> >> stimuli which results in fluctuations in the [0.2 0.8] Hz with respect
> >> to cognition of the subject.
>
> (snip)
>
> > Maybe I am missing something, but how can you hope to isolate a change
> > in a 0.2 Hz signal within a 2 second delay? &#4294967295;The wave period is 5
> > seconds and 2 seconds is less than half that. &#4294967295;This sounds pretty
> > tricky to me... but I'm not an expert at this.
>
> Isn't it the bandwidth, and not the frequency, that is important?
> If it is 0.2 to 0.8Hz then the bandwidth is 0.6Hz, which you might
> be able to do with 2s, though that is pretty close.
>
> So many years ago, when I would try to adjust the filters on
> a lock-in amplifier, such that the signal would come through,
> but not noise, and fast enough to be useful.
>
> 2 seconds will be pretty hard, I agree.
>
> > When you refer to a transition band between 200.1 and 200.2 that is
> > only 0.1 Hz which equates to a 10 second period. &#4294967295;There are
> > limitations to what you can do with only 2 seconds of data.
>
> -- glen

I don't think it is the bandwidth, it is the resolution that
determines the time period required.  I'm not really too sure what the
OP is really trying to do in terms of the signal processing.  He wants
to detect changes in a signals that lie in the 0.2 to 0.8 Hz
bandwidth.  If the signal ends up being a 0.2 Hz sine wave, I don't
think you can distinguish that from a 0.1 Hz sine wave with less than
10 seconds of data.

His concern is the noise that is present and you can't distinguish in
band noise from signal.  Removing out of band signal is done with a
band pass filter.  The steepness of the transition regions determines
the amount of data (duration) that must be examined, no?

Rick