def make_feature_vector(readings): # A function we apply to each group of power spectra ''' Create 100, log10-spaced bins for each power spectrum. For more on how this particular implementation works, see: http://coolworld.me/pre-processing-EEG-consumer-devices/ ''' return brainlib.avgPowerSpectrum(brainlib.binnedPowerSpectra(spectra(readings), 100), np.log10)
def make_feature_vector (readings): # A function we apply to each group of power spectra ''' Create 100, log10-spaced bins for each power spectrum. For more on how this particular implementation works, see: http://coolworld.me/pre-processing-EEG-consumer-devices/ ''' return brainlib.avgPowerSpectrum( brainlib.binnedPowerSpectra(spectra(readings), 100) , np.log10)
def bin_power_spectrum(power_spectrum): '''Create 100, log10-spaced bins for each power spectrum. See the work of Merrill et al.: Merrill, N., Maillart, T., Johnson, B., & Chuang, J. "Improving Physiological Signal Classification Using Logarithmic Quantization and a Progressive Calibration Technique." Physiological Computing Systems 2015. Angers, France.''' return(brainlib.binnedPowerSpectra(power_spectrum, 100))