How do I apply a Chebishev filter? I read a paper about a brain-computer interface. In this paper the authors reported "each signal has been filtered with an 8-order band-pass Chebishev Type I filter which cut-off frequencies are 0.1 and 10 Hz and has been decimated according to the high cut-off frequency". I tried to design this filter with scipy:

import scipy.signal as signal
signal.cheby1(8,0.05,[0.1,10.0],btype='band',analog=0,output='ba')


The result was:

Warning: invalid value encountered in sqrt
(array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
    nan,  nan,  nan,  nan,  nan,  nan]), array([ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
    nan,  nan,  nan,  nan,  nan,  nan]))


I have no background in signal processing, so I actually don't know what I am doing. I don't know whether they used a IIR or FIR filter or whether I have to scale the cut-off frequencies or whether I'm using the wrong ripple. I hope you can help me.
The main issue with the example you gave is that the filter design function cheby1 is returning all NaNs, which isn't going to be a very good filter. The problem is how you're specifying the passband/stopband edge frequencies. This particular function is meant to emulate MATLAB's cheby1 function; the frequencies that you give it should be normalized, such that a value of 1 corresponds to half of the sample rate.

import scipy.signal as signal
fs = whatever_the_sample_rate_of_the_filter_input_is_going_to_be
signal.cheby1(8,0.05,[0.1/(fs/2),10.0/(fs/2)],btype='band',analog=0,output='ba')


I don't have SciPy handy, but that should at least correctly design the filter you want.