Common Average Re-Referencing

basic.car.run(data)

Applies common average re-referencing.

Parameters:

data – Channels x samples.

Returns:

Common average re-referenced data.

The following example shows how to apply car.

import numpy as np
import finn.basic.common_average_rereferncing as car

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

import random

def main():
   #Configure sample data
   channel_count = 256
   frequency = [random.randint(5, 50) for _ in range(channel_count)]
   data_range = np.arange(0, 10000)
   frequency_sampling = 200

   #Configure noise data
   frequency_noise = 50
   shared_noise_strength = 10
   random_noise_strength = 1

   #Generate some sample data
   raw_data = [None for _ in range(channel_count)]
   for idx in range(channel_count):
       genuine_signal = np.sin(2 * np.pi * frequency[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[idx] = genuine_signal + shared_noise_signal + random_noise_signal
   raw_data = np.asarray(raw_data)

   car_data = car.run(raw_data)

   #visualize result
   (fig, axes) = plt.subplots(3, 2)
   fig.suptitle("Peaks are supposedly at %i, %i and %iHz" % (frequency[0], frequency[1], frequency[2]))
   for idx in range(3):
       axes[idx, 0].psd(raw_data[idx, :], NFFT = frequency_sampling, Fs = frequency_sampling)
       axes[idx, 1].psd(car_data[idx, :], NFFT = frequency_sampling, Fs = frequency_sampling)

   plt.show(block = True)

 main()

While there is an artifact at 50Hz on the left side (pre car application), this artifact is gone on the right side. As this artifact was introduced by the shared reference, applying a car filter removed the artifact: