Decoding hand movements from ECoG recordings
git clone https://github.com/BruntonUWBio/ecog-hand
sudo apt-get install python3-pip
sudo pip3 install numpy matplotlib cvxpy pytest sklearnIn your project, load the required modules:
%matplotlib inline
from mrDMD import mrDMD
from DMD import DMD
from helper_functions import *Start with a signal composed of a sum of sinusoids in 1-dimension
dt = 1/200
N = 1000
t = np.linspace(0, 5, N)
amp = 1
freq_max = 40
freqs = np.arange(freq_max)
freqs = freqs[::4]
print('Freqs: ')
print(freqs)
X = buildX(freqs, t)
plt.figure()
plt.plot(t, X[0,:])
plt.show()Freqs:
[ 0 4 8 12 16 20 24 28 32 36]
Comparisons to FFT when frequency well below Nyquist
freq, P = fftPlot(X, dt, freq_max)
stack_factor = 2*len(freqs)
kwargs = {'dt':dt,
'scale_modes':True}
dmd = DMD(**kwargs)
dmd.fit(X)
f, P = dmd.spectrum(sort='frequencies')
idx = in_range(f, (1,freq_max))
plt.figure()
plt.stem([f[i] for i in idx], [P[i] for i in idx])
plt.title('DMD spectrum shortened')
plt.xlabel('Frequency')
plt.show()
plt.close()
Pytest is used for testing run the following at the command line
pytestFor DMD algorithm details see:
- "Dynamic Mode Decomposition". J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, and Joshua L. Proctor 2016.
For multiresolution DMD algorithm details see:
- "Multiresolution Dynamic Mode Decomposition". J. Nathan Kutz, Xing Fu, Steven L. Brunton 2016.
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