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I have EEG data recorded with 128Hz sampling rate. As my goal is to reduce the amount of data (and maybe noise), I want to downsample the data to 64Hz (I am only interested in range 0.5 - 30Hz).

I would perform downsampling (64Hz) and use a bandpassfilter (0.5 - 30Hz).

Now my question is, what is the best order to perform those two steps and why? Which impacts will these methods have to the quality of my data?

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    $\begingroup$ It's also possible to combine the two using a polyphase filter. This can save some compute overhead by not computing values that will be discarded. $\endgroup$ – alex.forencich Dec 21 '16 at 22:35
  • $\begingroup$ @alex.forencich Good point, I will consider that. Are there any disadvantages for this kind of filter? $\endgroup$ – ppasler Dec 22 '16 at 8:32
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You need to filter first and then downsample. Otherwise, you will run into aliasing problems. I.e. frequencies that are above 30 Hz will create images within your frequencies of interest. You can consider the little script below to compare both methods:

Fs = 128.0
t = np.arange(0, 10, 1/Fs)
signal = np.sin(2*np.pi*10*t) + np.sin(2*np.pi*50*t)
sigma2 = 0.5

rx = signal + np.sqrt(sigma2) * np.random.randn(len(signal))

downsampled = rx[::2]
(b1, a1) = scipy.signal.butter(6, 30/(Fs/4))
down_and_filtered = scipy.signal.lfilter(b1, a1, downsampled)

(b2, a2) = scipy.signal.butter(6, 30/(Fs/2))
filtered_and_down = scipy.signal.lfilter(b2, a2, rx)[::2]
t2 = t[::2]


plt.figure(figsize=(20,20))
plt.subplot(221)
plt.plot(t, rx)
plt.plot(t, signal)
plt.xlim((1, 1.3))
plt.title('Received signal')

plt.subplot(222)
plt.plot(t2, down_and_filtered)
plt.plot(t2, filtered_and_down)
plt.title('Compare of both methods in time domain')
plt.xlim((1,1.3))

f = np.linspace(-Fs/2, Fs/2, 4*len(t))
plt.subplot(223)
plt.plot(f, np.fft.fftshift(abs(np.fft.fft(rx, 4*len(t)))))
plt.title('RX spectrum. Note the two peaks, one is interference, having frequency over 30Hz')

f2 = np.linspace(-Fs/4, Fs/4, 4*len(t2))
plt.subplot(224)
plt.plot(f2, np.fft.fftshift(abs(np.fft.fft(down_and_filtered, 4*len(t2)))), '-o')
plt.plot(f2, np.fft.fftshift(abs(np.fft.fft(filtered_and_down, 4*len(t2)))))
plt.title('Spectrum of both alternatives. NOte the blue curve has a wrong component')

program output

In this script you have a signal that is composed of two sines of frequency 10 and 50Hz plus some noise. In the FFT of the full signal, both spectral lines clearly occur. Now, what you want to have after your signal processing is only one spectral line at 10Hz (because the 50Hz signal should be filtered out). HOwever, if you downsample first, the 50Hz wave is mirrored to $14Hz=64Hz-50Hz$ and cant be filtered out subsequently. Hence, you need to do the filtering before and then downsample your signal.

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  • $\begingroup$ Thank you! Your explanation and the plots makes it easy to understand. $\endgroup$ – ppasler Dec 20 '16 at 13:57
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    $\begingroup$ To avoid getting angry comments from referees, @ppasler should also carefully think what kind of filter to use. For example, for some neuroscience research questions, it would be catastrophic if the filter is not zero-phase. $\endgroup$ – mmh Dec 20 '16 at 18:59
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    $\begingroup$ ... and if you are hoping that the low-pass takes care of the line noise at 50 or 60 Hz as well, please carefully check whether the filter provides enough attenuation at those frequencies. $\endgroup$ – mmh Dec 20 '16 at 19:00
  • $\begingroup$ IMO proper downsampling includes low-pass filtering. $\endgroup$ – leftaroundabout Dec 21 '16 at 0:19
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    $\begingroup$ @leftaroundabout Absolutely - the point is to remove the part of the signal for which the downsampling does not work at all, and creates only garbage. Unfourtunately, the garbage only looks like random noise in the best case, but practically it is well structured. One common example is the Moiree pattern. It consists of nothing as this garbage. $\endgroup$ – Volker Siegel Dec 21 '16 at 4:17
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Since I can't comment on this particular site I'd say this, consider the following before you do what you're trying to do.

Due to the Nyquist law you want your sampling frequency to be that of the DOUBLE of the maximum frequency your analog signal has. If you downsample to 64 hz that means you'll only be able to see signal data up to 32 Hz. EEG contains the gamma band above that frequency in the 30~50 range, which is WHY it is sampled at 128 Hz to begin with. You need at least 100 Hz to get all of EEG. That is of course, unless you want to get rid of gamma band.

Example: http://journal.frontiersin.org/article/10.3389/fnhum.2013.00056/full

Site admin: Feel free to move this to a comment in the original question or given answer. Thank you!

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  • $\begingroup$ Good point, I am aware of this fact. I am not interested in the gamma band, so it's no problem to remove it. $\endgroup$ – ppasler Dec 21 '16 at 11:01

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