docs: def plot_emg_graphical_statistical(time, signal, max_sample_value, min_sample_value, avg_sample_value,
                                     std_sample_value):
      """
      -----
      Brief
      -----
      This plotting function ensures a graphical representation of maximum, minimum and average sample
      values registered on the entire EMG acquisition.
  
      -----------
      Description
      -----------
      Function intended to generate a single Bokeh figure with graphically describing and identifying
      some statistical parameters extracted from the analysis of the entire electromyographic (EMG) signal.
  
      Applied in the Notebook titled "EMG Analysis - Time and Frequency Parameters".
  
      ----------
      Parameters
      ----------
      time : list
          Time-axis linked to the acquired EMG signal samples.
  
      signal : list
          Acquired EMG signal samples.
  
      max_sample_value : float
          Maximum value registered in the acquired EMG samples.
  
      min_sample_value: float
          Minimum value registered in the acquired EMG samples.
  
      avg_sample_value : float
          Average value registered in the acquired EMG samples.
  
      std_sample_value : int
          Standard deviation of the acquired EMG sample values relatively to avg_sample_value.
      """
  
      # List that store the figure handler
      list_figures = []
  
      # Plotting of EMG.
      list_figures.append(figure(x_axis_label='Time (s)', y_axis_label='Electric Tension (mV)', x_range=(0, time[-1] + 0.50 * time[-1]), y_range=[-1.10, 1], **opensignals_kwargs("figure")))
      list_figures[-1].line(time, signal, legend="EMG Signal", **opensignals_kwargs("line"))
  
      # Representation of EMG and the determined parameters
      parameter_list = ["Maximum", "Minimum", "Average", "Standard Deviation"]
      for parameter in parameter_list:
          find_time_max = numpy.array(time)[numpy.where(numpy.array(signal) == max_sample_value)]
          find_time_min = numpy.array(time)[numpy.where(numpy.array(signal) == min_sample_value)]
          if parameter == "Maximum":
              list_figures[-1].circle(find_time_max, max_sample_value, radius = 0.5, fill_color=opensignals_color_pallet(),
                                      legend=parameter + " EMG")
          elif parameter == "Minimum":
              list_figures[-1].circle(find_time_min, min_sample_value, radius=0.5, fill_color=opensignals_color_pallet(),
                                      legend=parameter + " EMG")
          elif parameter == "Average":
              list_figures[-1].line([0, time[-1]], [avg_sample_value, avg_sample_value],
                                    legend=parameter + " EMG Sample", **opensignals_kwargs("line"))
          elif parameter == "Standard Deviation":
              box_annotation = BoxAnnotation(left=0, right=time[-1], top=avg_sample_value + std_sample_value,
                                             bottom=avg_sample_value - std_sample_value, fill_color="black",
                                             fill_alpha=0.3)
              list_figures[-1].rect(find_time_min, std_sample_value, width=0, height=0, fill_color="black", fill_alpha=0.3,
                                    legend="Average + Standard Deviation Zone")
              list_figures[-1].add_layout(box_annotation)
  
      # Show figure.
      opensignals_style(list_figures)
      show(list_figures[-1])