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trainClassifierCyton.py
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import os
import numpy as np
from scipy.signal import *
import csv
import matplotlib.pyplot as plt
from scipy import signal
from brainflow.board_shim import BoardShim, BrainFlowInputParams, LogLevels, BoardIds
from brainflow.data_filter import DataFilter, FilterTypes, AggOperations, WindowFunctions, DetrendOperations
from sklearn.cluster import KMeans
def nextpow2(i):
"""
Find the next power of 2 for number i
"""
n = 1
while n < i:
n *= 2
return n
def d_2d_to_3d(x, agg_num, hop):
# Pad to at least one block.
len_x, n_in = x.shape
if (len_x < agg_num): #not in get_matrix_data
x = np.concatenate((x, np.zeros((agg_num - len_x, n_in))))
# main 2d to 3d.
len_x = len(x) #15344
i1 = 0
x3d = []
while (i1 + agg_num <= len_x):
x3d.append(x[i1 : i1 + agg_num])
i1 += hop
return np.array(x3d)
def Kmeans8D():
#Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB'
data_folder = 'EEG-IO'
# Parameters and bandpass filtering
fs = 250.0
# Reading data files
file_idx = 0
list_of_files = [f for f in os.listdir(data_folder) if os.path.isfile(os.path.join(data_folder, f)) and '_data' in f] #List of all the files, Lists are randomized, its only looking for file with _data in it
print(list_of_files)
file_sig = list_of_files[file_idx] # Data File
file_stim = list_of_files[file_idx].replace('_data','_labels') #Label File, Replacing _data with _labels
print ("Reading: ", file_sig, file_stim)
# Loading data
if data_folder == 'EEG-IO' or data_folder == 'EEG-MB':
data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=";", skiprows=1, usecols=(0,1,2)) #data_sig would be a buffer
elif data_folder == 'EEG-VR' or data_folder == 'EEG-VV':
data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=",", skiprows=5, usecols=(0,1,2))
data_sig = data_sig[0:(int(200*fs)+1),:] # getting data ready -- not needed for previous 2 datasets
data_sig = data_sig[:,0:3] #
data_sig[:,0] = np.array(range(0,len(data_sig)))/fs
############ Calculating PSD ############
index, ch = data_sig.shape[0], data_sig.shape[1]
# print(index)
feature_vectors = [[], []]
feature_vectorsa = [[], []]
x=1
while x>0 and x<3:
if x==1:
data_sig[:,1] = lowpass(data_sig[:,1], 10, fs, 4)
elif x==2:
data_sig[:,2] = lowpass(data_sig[:,2], 10, fs, 4)
for y in range(500, 19328 ,500):
#print(ch)
if x==1:
DataFilter.detrend(data_sig[y-500:y, 1], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data_sig[y-500:y, 1], nfft, nfft//2, 250,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
# Theta 4-8
band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
#Alpha 8-12
band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
#Beta 12-30
band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
# print(feature_vectors.shape)
feature_vectors[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
feature_vectorsa[x].insert(y, [band_power_delta, band_power_theta])
elif x==2:
DataFilter.detrend(data_sig[y-500:y, 2], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data_sig[y-500:y, 2], nfft, nfft//2, 250,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
# Theta 4-8
band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
#Alpha 8-12
band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
#Beta 12-30
band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
# print(feature_vectors.shape)
# feature_vectorsc[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
# feature_vectorsd[x].insert(y, [band_power_delta, band_power_theta])
x = x+1
print(feature_vectorsa)
powers = np.log10(np.asarray(feature_vectors, dtype=float))
powers1 = np.log10(np.asarray(feature_vectorsa, dtype=float))
# powers2 = np.log10(np.asarray(feature_vectorsb))
# powers3 = np.log10(np.asarray(feature_vectorsc))
print(powers.shape)
print(powers1.shape)
powers = powers.reshape(598, 4*2)
kmeans = KMeans(n_clusters=2, random_state=0)
kmeans.fit(powers)
return kmeans
# clusters = kmeans.fit(powers)
# centers = kmeans.cluster_centers_
# covMatrix = np.cov(centers.T)
# corMatrix = np.corrcoef(centers.T)
# # sn.heatmap(covMatrix, annot=True, fmt='g')
# # plt.show()
# sn.heatmap(corMatrix, annot=True, fmt='g')
# plt.show()
# plt.scatter(powers[:, 2], powers[:, 3], c=clusters, s=50,
# cmap='viridis')
# # print(kmeans.cluster_centers_[:, :])
# plt.scatter(centers[:, 2], centers[:, 3], c='black', s=200, alpha=0.5)
# plt.show()
def Kmeans4D():
#Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB'
data_folder = 'EEG-IO'
# Parameters and bandpass filtering
fs = 250.0
# Reading data files
file_idx = 0
list_of_files = [f for f in os.listdir(data_folder) if os.path.isfile(os.path.join(data_folder, f)) and '_data' in f] #List of all the files, Lists are randomized, its only looking for file with _data in it
print(list_of_files)
file_sig = list_of_files[file_idx] # Data File
file_stim = list_of_files[file_idx].replace('_data','_labels') #Label File, Replacing _data with _labels
print ("Reading: ", file_sig, file_stim)
# Loading data
if data_folder == 'EEG-IO' or data_folder == 'EEG-MB':
data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=";", skiprows=1, usecols=(0,1,2)) #data_sig would be a buffer
elif data_folder == 'EEG-VR' or data_folder == 'EEG-VV':
data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=",", skiprows=5, usecols=(0,1,2))
data_sig = data_sig[0:(int(200*fs)+1),:] # getting data ready -- not needed for previous 2 datasets
data_sig = data_sig[:,0:3] #
data_sig[:,0] = np.array(range(0,len(data_sig)))/fs
############ Calculating PSD ############
index, ch = data_sig.shape[0], data_sig.shape[1]
# print(index)
feature_vectors = [[], []]
feature_vectorsa = [[], []]
x=1
while x>0 and x<3:
if x==1:
data_sig[:,1] = lowpass(data_sig[:,1], 10, fs, 4)
elif x==2:
data_sig[:,2] = lowpass(data_sig[:,2], 10, fs, 4)
for y in range(500, 19328 ,500):
#print(ch)
if x==1:
DataFilter.detrend(data_sig[y-500:y, 1], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data_sig[y-500:y, 1], nfft, nfft//2, 250,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
# Theta 4-8
band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
#Alpha 8-12
band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
#Beta 12-30
band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
# print(feature_vectors.shape)
feature_vectors[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
feature_vectorsa[x].insert(y, [band_power_delta, band_power_theta])
elif x==2:
DataFilter.detrend(data_sig[y-500:y, 2], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data_sig[y-500:y, 2], nfft, nfft//2, 250,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
# Theta 4-8
band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
#Alpha 8-12
band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
#Beta 12-30
band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
# print(feature_vectors.shape)
# feature_vectorsc[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
# feature_vectorsd[x].insert(y, [band_power_delta, band_power_theta])
x = x+1
print(feature_vectorsa)
powers = np.log10(np.asarray(feature_vectors, dtype=float))
powers1 = np.log10(np.asarray(feature_vectorsa, dtype=float))
# powers2 = np.log10(np.asarray(feature_vectorsb))
# powers3 = np.log10(np.asarray(feature_vectorsc))
print(powers.shape)
print(powers1.shape)
powers = powers.reshape(598, 4*2)
powers = powers[:, 2:6]
kmeans = KMeans(n_clusters=3, random_state=0)
kmeans.fit(powers)
return kmeans
# clusters = kmeans.fit(powers)
# centers = kmeans.cluster_centers_
# covMatrix = np.cov(centers.T)
# corMatrix = np.corrcoef(centers.T)
# # sn.heatmap(covMatrix, annot=True, fmt='g')
# # plt.show()
# sn.heatmap(corMatrix, annot=True, fmt='g')
# plt.show()
# plt.scatter(powers[:, 2], powers[:, 3], c=clusters, s=50,
# cmap='viridis')
# # print(kmeans.cluster_centers_[:, :])
# plt.scatter(centers[:, 2], centers[:, 3], c='black', s=200, alpha=0.5)
# plt.show()
def Kmeans2D():
def Kmeans4D():
#Options to read: 'EEG-IO', 'EEG-VV', 'EEG-VR', 'EEG-MB'
data_folder = 'EEG-IO'
# Parameters and bandpass filtering
fs = 250.0
# Reading data files
file_idx = 0
list_of_files = [f for f in os.listdir(data_folder) if os.path.isfile(os.path.join(data_folder, f)) and '_data' in f] #List of all the files, Lists are randomized, its only looking for file with _data in it
print(list_of_files)
file_sig = list_of_files[file_idx] # Data File
file_stim = list_of_files[file_idx].replace('_data','_labels') #Label File, Replacing _data with _labels
print ("Reading: ", file_sig, file_stim)
# Loading data
if data_folder == 'EEG-IO' or data_folder == 'EEG-MB':
data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=";", skiprows=1, usecols=(0,1,2)) #data_sig would be a buffer
elif data_folder == 'EEG-VR' or data_folder == 'EEG-VV':
data_sig = np.loadtxt(open(os.path.join(data_folder,file_sig), "rb"), delimiter=",", skiprows=5, usecols=(0,1,2))
data_sig = data_sig[0:(int(200*fs)+1),:] # getting data ready -- not needed for previous 2 datasets
data_sig = data_sig[:,0:3] #
data_sig[:,0] = np.array(range(0,len(data_sig)))/fs
############ Calculating PSD ############
index, ch = data_sig.shape[0], data_sig.shape[1]
# print(index)
feature_vectors = [[], []]
feature_vectorsa = [[], []]
x=1
while x>0 and x<3:
if x==1:
data_sig[:,1] = lowpass(data_sig[:,1], 10, fs, 4)
elif x==2:
data_sig[:,2] = lowpass(data_sig[:,2], 10, fs, 4)
for y in range(500, 19328 ,500):
#print(ch)
if x==1:
DataFilter.detrend(data_sig[y-500:y, 1], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data_sig[y-500:y, 1], nfft, nfft//2, 250,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
# Theta 4-8
band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
#Alpha 8-12
band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
#Beta 12-30
band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
# print(feature_vectors.shape)
feature_vectors[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
feature_vectorsa[x].insert(y, [band_power_delta, band_power_theta])
elif x==2:
DataFilter.detrend(data_sig[y-500:y, 2], DetrendOperations.LINEAR.value)
psd = DataFilter.get_psd_welch(data_sig[y-500:y, 2], nfft, nfft//2, 250,
WindowFunctions.BLACKMAN_HARRIS.value)
band_power_delta = DataFilter.get_band_power(psd, 1.0, 4.0)
# Theta 4-8
band_power_theta = DataFilter.get_band_power(psd, 4.0, 8.0)
#Alpha 8-12
band_power_alpha = DataFilter.get_band_power(psd, 8.0, 12.0)
#Beta 12-30
band_power_beta = DataFilter.get_band_power(psd, 12.0, 30.0)
# print(feature_vectors.shape)
# feature_vectorsc[x].insert(y, [band_power_delta, band_power_theta, band_power_alpha, band_power_beta])
# feature_vectorsd[x].insert(y, [band_power_delta, band_power_theta])
x = x+1
print(feature_vectorsa)
powers = np.log10(np.asarray(feature_vectors, dtype=float))
powers1 = np.log10(np.asarray(feature_vectorsa, dtype=float))
# powers2 = np.log10(np.asarray(feature_vectorsb))
# powers3 = np.log10(np.asarray(feature_vectorsc))
print(powers.shape)
print(powers1.shape)
powers = powers.reshape(598, 4*2)
powers = powers[:, 3:5]
kmeans = KMeans(n_clusters=3, random_state=0)
kmeans.fit(powers)
return kmeans
# clusters = kmeans.fit(powers)
# centers = kmeans.cluster_centers_
# covMatrix = np.cov(centers.T)
# corMatrix = np.corrcoef(centers.T)
# # sn.heatmap(covMatrix, annot=True, fmt='g')
# # plt.show()
# sn.heatmap(corMatrix, annot=True, fmt='g')
# plt.show()
# plt.scatter(powers[:, 2], powers[:, 3], c=clusters, s=50,
# cmap='viridis')
# # print(kmeans.cluster_centers_[:, :])
# plt.scatter(centers[:, 2], centers[:, 3], c='black', s=200, alpha=0.5)
# plt.show()