Bad channel identification
==========================


.. currentmodule:: cleansing.bad_channel_identification
.. autofunction:: run

The following code example shows how to apply bad channel identification & subsequent restoration.

.. code:: python
    
   import numpy as np
   import random

   import matplotlib
   matplotlib.use("Qt5agg")
   import matplotlib.pyplot as plt

   import finn.cleansing.bad_channel_identification as bci
   import finn.cleansing.channel_restoration as cr

   def main():
       #Configure sample data
       channel_count = 64
       frequency = [random.randint(5, 50) for _ in range(channel_count)]
       data_range = np.arange(0, 10000)
       frequency_sampling = 200
       ch_names = ['O1', 'Oz', 'O2', 'PO9', 'PO7', 'PO3', 'POz', 'PO4', 'PO8', 'PO10', 'P9', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'P10',
                  'TP9', 'TP7', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'CP6', 'TP8', 'TP10', 'T9', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'T8', 'T10',
                  'FT9', 'FT7', 'FC5', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'FC6', 'FT8', 'FT10', 'F9', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'F10',
                  'AF9', 'AF7', 'AF3', 'AFz', 'AF4', 'AF8', 'AF10', 'Fp1', 'Fpz', 'Fp2']
       
       #Configure noise data
       frequency_noise = 50
       shared_noise_strength = 1
       random_noise_strength = 1
       
       #Configure bad channel
       bad_channel_idx = 1
       bad_channel_signal_power = 1.1
       
       #Generate some sample data
       raw_data = [None for _ in range(channel_count)]
       for channel_idx in range(channel_count):
           genuine_signal = np.sin(2 * np.pi * frequency[channel_idx] * data_range / frequency_sampling)
           shared_noise_signal = np.sin(2 * np.pi * frequency_noise * data_range / frequency_sampling) * shared_noise_strength
           random_noise_signal = np.random.random(len(data_range)) * random_noise_strength
           
           raw_data[channel_idx] = genuine_signal + shared_noise_signal + random_noise_signal
       
       raw_data[bad_channel_idx] = np.random.random(len(data_range)) * bad_channel_signal_power
       #raw_data = np.asarray(raw_data)
       
       #Faulty channel gets identified
       (_, invalid_list, _) = bci.run(raw_data, ch_names, [frequency_sampling for _ in range(channel_count)], [[60, 100]], broadness = 3, visual_inspection = True)
       #Faulty channel gets substituted via neighbors
       rest_data = cr.run(raw_data, ch_names, invalid_list)
       
       #visualization
       channels_to_plot = 3
       (_, axes) = plt.subplots(channels_to_plot, 2)
       for channel_idx in range(channels_to_plot):
           axes[channel_idx, 0].plot(raw_data[channel_idx][:200])
           axes[channel_idx, 1].plot(rest_data[channel_idx][:200])
           
       axes[0, 0].set_title("before correction")
       axes[0, 1].set_title("after correction")
       
       axes[0, 0].set_ylabel("Channel #0\n"); axes[0, 0].set_yticks([-2, 0, 2])
       axes[1, 0].set_ylabel("Channel #1\n(faulty channel)"); axes[1, 0].set_yticks([-2, 0, 2])
       axes[2, 0].set_ylabel("Channel #2\n"); axes[2, 0].set_yticks([-2, 0, 2])
       
       plt.show()

   main()

Applying bad channel identification automatically selected channels whose broadband power is more than two standard deviations different from other channels. Yet, manual optimization of the selection is possible (and recommended). Manual adjustments can be performed in the screen below. 
    
.. image:: img/bad_channel_identification.png