--- a
+++ b/eeg_pipeline.py
@@ -0,0 +1,100 @@
+import mne
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import scipy.io
+import os
+from collections import OrderedDict
+
+def standardize_sensors(raw_data, channel_config, return_montage=True):
+
+	# channel_names = [x.upper() for x in raw_data.ch_names]
+	
+	NUM_REDUCED_SENSORS = 8
+	montage_sensor_set = ["F7", "F3", "F8", "F4", "T3", "C3", "T4", "C4", "T5", "P3", "T6", "P4", "O1", "O2"]
+	first = ["F7", "F8", "T3", "T4", "T5", "T6", "O1", "O2"]
+	second = ["F3", "F4", "C3", "C4", "P3", "P4", "P3", "P4"]
+	
+	if channel_config in ["01_tcp_ar", "03_tcp_ar_a"]:
+		montage_sensor_set = [str("EEG "+x+"-REF") for x in montage_sensor_set]
+		first = [str("EEG "+x+"-REF") for x in first]
+		second = [str("EEG "+x+"-REF") for x in second]
+
+	elif channel_config == "02_tcp_le":
+		montage_sensor_set = [str("EEG "+x+"-LE") for x in montage_sensor_set]
+		first = [str("EEG "+x+"-LE") for x in first]
+		second = [str("EEG "+x+"-LE") for x in second]
+
+	raw_data = raw_data.pick_channels(montage_sensor_set, ordered=True)
+
+	# return channels without subtraction - 14 of them
+	if return_montage == False:
+		return raw_data, raw_data
+
+	# use a sensor's data to get total number of samples
+	reduced_data = np.zeros((NUM_REDUCED_SENSORS, raw_data.n_times))
+
+	# create derived channels
+	for idx in range(NUM_REDUCED_SENSORS):
+		reduced_data[idx, :] = raw_data[first[idx]][0][:] - raw_data[second[idx]][0][:]
+	
+	# create new info object for reduced sensors
+	reduced_info = mne.create_info(ch_names=[
+											"F7-F3", "F8-F4",
+											"T3-C3", "T4-C4",
+											"T5-P3", "T6-P4",
+											"O1-P3", "O2-P4"
+											], sfreq=raw_data.info["sfreq"], ch_types=["eeg"]*NUM_REDUCED_SENSORS)
+	
+	# https://mne.tools/dev/auto_examples/io/plot_objects_from_arrays.html?highlight=rawarray
+	reduced_raw_data = mne.io.RawArray(reduced_data, reduced_info)
+	# return reduced_raw_data, raw_data
+	return reduced_raw_data
+
+
+def downsample(raw_data, freq=250):
+	raw_data = raw_data.resample(sfreq=freq)
+	return raw_data, freq
+
+
+def highpass(raw_data, cutoff=1.0):
+	raw_data.filter(l_freq=cutoff, h_freq=None)
+	return raw_data
+
+
+def remove_line_noise(raw_data, ac_freqs = np.arange(50, 101, 50)):
+	raw_data.notch_filter(freqs=ac_freqs, picks="eeg", verbose=False)
+	return raw_data
+
+# accepts PSD of all sensors, returns band power for all sensors
+def get_brain_waves_power(psd_welch, freqs):
+
+	brain_waves = OrderedDict({
+		"delta" : [1.0, 4.0],
+		"theta": [4.0, 7.5],
+		"alpha": [7.5, 13.0],
+		"lower_beta": [13.0, 16.0],
+		"higher_beta": [16.0, 30.0],
+		"gamma": [30.0, 40.0]
+	})
+
+	# create new variable you want to "fill": n_brain_wave_bands
+	band_powers = np.zeros((psd_welch.shape[0], 6))
+
+	for wave_idx, wave in enumerate(brain_waves.keys()):
+		# identify freq indices of the wave band
+		if wave_idx == 0:
+			band_freqs_idx = np.argwhere((freqs <= brain_waves[wave][1]))
+		else:
+			band_freqs_idx = np.argwhere((freqs >= brain_waves[wave][0]) & (freqs <= brain_waves[wave][1]))
+
+		# extract the psd values for those freq indices
+		band_psd = psd_welch[:, band_freqs_idx.ravel()]
+
+		# sum the band psd data to get total band power
+		total_band_power = np.sum(band_psd, axis=1)
+
+		# set power in band for all sensors
+		band_powers[:, wave_idx] = total_band_power    
+
+	return band_powers
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