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stream_online.py
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stream_online.py
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import numpy as np
import os
import classificador_online as cl_online
import aquisicao as aq
import filtros as filt
import Fcar as fc
import time as tm
import sys; sys.path.append('..')
from openbci import cyton as bci
import logging
from scipy import signal
from scipy.fftpack import fft
import matplotlib.pyplot as plt
n_freq=4 #Número de frequências
n_trial=8 #aquisições feitas
freq=np.array([6,10,12,15]) # Frequências
n_elec=8 #Número de eletrodos
t_sinal=3 #Tempo de coleta
t_online=15
fs=250 #Frequência de amostragem
t_ini=2 #tempo inicial de aquisição
trial=[1,2,3,4,5,6,7,8]
n_win=4 #Número de janelas
per_train=0.75 #Porcentagem de treinamento
individuo='Teste' #Nome do individuo
create_win=0
teste_class=0
n_signal=n_trial*n_win*n_freq #número de sinais
dim_sig=n_elec*n_freq #número de sinais por eletrodo
cont_aq=-1
cont=-1
Data=np.zeros([fs*t_sinal,n_elec])
Data_f=np.zeros([fs*t_sinal,n_elec])
Data_f_all=np.zeros([fs*t_online,n_elec])
Data_aq=np.zeros([fs*t_online,n_elec])
X=np.zeros([1,n_freq*n_elec+1])
cont_time=0
n_samples_aq=fs*t_online
n_samples=fs*t_sinal
#teste de valores
cont_freq=0
freq_teste=12
#Filtro passa banda 5 à 50 Hz
f_low=5
f_high=50
filter_order=8
nyq = 0.5 * fs
low = f_low / nyq
high = f_high / nyq
b_PB, a_PB = signal.butter(filter_order, [low, high], btype='band')
#Filtro notch 60 Hz
Q=30.0
w0 = 60/(fs/2)
b_FN, a_FN=signal.iirnotch(w0,Q);
# treino e filtro
treino=1
aplica_filtro=1
classifica_online=0
teste_aq_online=0
compara_cod=1
#Funções
def start_stream(board): #Inicia a gravacao
global cont
global cont_aq
cont_aq=-1
cont=-1 #inicia cont
board.start_streaming(return_Data)
board.stop
board.print_bytes_in()
def return_Data(sample):
global n_aq,n_freq
global fs
global t_sinal
global cont
global cont_aq
global n_samples
global Data
global b_PB,a_PB
global b_FN,a_FN
global X,R,w
global Data_f,Data_f_all
global cont_time
global cont_freq
global freq_teste
vet_aux=np.zeros([1,4])
Data[cont,:]=sample.channel_data
Data_aq[cont_aq,:]=Data[cont,:]
cont=cont+1
cont_aq=cont_aq+1
L_cont=0
xf=np.linspace(0,30,t_sinal*30)
if cont==n_samples:
cont_time=cont_time+1
for elec in range(n_elec):
signal_f=signal.lfilter(b_PB,a_PB,Data_aq[:,elec])
signal_f=signal.lfilter(b_FN,a_FN,signal_f)
Data_f[:,elec]=signal_f[((cont_time-1)*t_sinal*fs)\
:cont_time*t_sinal*fs]
Data_f_all[:,elec]=signal_f
Data_f_c=fc.car_filter(Data_f,n_elec,t_sinal,fs)
L_cont=0
for e in range(0,n_elec): # Para cada eletrodo, fft calculada
sig=Data_f_c[:,e]
sig_FFT=np.abs(fft(sig))/max(np.abs((fft(sig))))
for fr in freq:
ind_fr=fr*(t_sinal)
ind_fr=int(ind_fr)
X[0,L_cont]=sig_FFT[ind_fr-1]
L_cont=L_cont+1
X[0,n_freq*n_elec]=1
y=cl_online.return_y(X,w,n_freq)
fr_result=cl_online.result(y,n_freq)
print('\n Frequência: '+str(fr_result)+'Hz \n')
if freq_teste==fr_result:
cont_freq=cont_freq+1
cont=0
Data=Data*0
if cont_aq==n_samples_aq:
print('\n Aquisição: '+str(cont_aq)+'\n')
os.chdir('/home/pi/Desktop/BCI/online/scripts/Data_record/'+individuo)
np.save('Data_record',Data_f_all)
os.chdir('/home/pi/Desktop/BCI/online/scripts/')
sys.exit()
def stream_data():
n_freq=4 #Número de frequências
n_trial=1 #Número de trials
freq=np.array([6,10,12,15]) #frequências
n_electrodes=8 #Número de eletrodos
t_collect=3 #tempo de coleta (12 segundos) e 2 segundos para descarte
fs=250 #Frequência de amostragem
n_samples=fs*t_collect #Número de amostras
Data=np.zeros([n_samples,n_electrodes]) #Matriz de dados
#auxiliares:
freq_select=0
trial_select=0
cont=0
filtro_car=1
filtro=1
start_arm=1
time_trash=3
individuo='Teste' #Nome do individuo
if __name__ == '__main__':
# port = '/dev/tty.OpenBCI-DN008VTF'
#port = '/dev/tty.usbserial-DB00JAM0'
# port = '/dev/tty.OpenBCI-DN0096XA'
port = '/dev/ttyUSB0' #port
baud = 115200 #bits
logging.basicConfig(filename="test.log"\
,format='%(asctime)s - %(levelname)s : %(message)s',level=logging.DEBUG)
logging.info('---------LOG START-------------')
board = bci.OpenBCICyton(port=port, scaled_output=False, log=True)
print("Board Instantiated")
board.ser.write(str.encode('allon'))
tm.sleep(3)
#Cria e acessa pastas
print("\n Press ENTER to start \n");
OP=input()
matriz_Data=start_stream(board)
return matriz_Data
#Principal
if treino==1:
runfile('/home/pi/Desktop/BCI/online/scripts/stream_time.py',\
wdir='/home/pi/Desktop/BCI/online/scripts/')
if aplica_filtro==1:
runfile('/home/pi/Desktop/BCI/online/scripts/aplica_filtro.py',\
wdir='/home/pi/Desktop/TCC/scripts/treino')
runfile('/home/pi/Desktop/TCC/scripts/treino/main.py',\
wdir='/home/pi/Desktop/TCC/scripts/treino')
if classifica_online==1:
R=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT/R.npy')
w=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT/w.npy')
stream_data()
if teste_aq_online==1:
R=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT/R.npy')
w=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT/w.npy')
sinal6=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_6Hz.npy')
sinal10=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_10Hz.npy')
sinal12=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_12Hz.npy')
sinal15=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_15Hz.npy')
sinal=fc.car_filter(sinal15,n_elec,t_online,fs)
amostra_freq=t_sinal*18
amostra_temp=t_sinal*fs
ini=750;
fim=ini+750;
aux=int(t_online/t_sinal)
mat_sig=np.zeros([fs*t_sinal,n_elec])
for e in range(0,aux-1):
fig, axs=plt.subplots(2)
xt=np.linspace(0,t_sinal,amostra_temp)
xf=np.linspace(0,18,amostra_freq)
sig=sinal[ini:fim,1]
sig_FFT=np.abs(fft(sig))/max(np.abs((fft(sig))))
axs[0].plot(xt,sig)
axs[0].set(xlabel='Tempo (s)', ylabel='Amplitude')
axs[0].set_title('Sinal '+str(e))
axs[1].plot(xf,sig_FFT[0:amostra_freq],'r')
axs[1].set(xlabel='Frequência (Hz)', ylabel='Magnitude')
plt.show()
del fig,axs
ini=ini+750
fim=fim+750
ini=750
fim=ini+750
for k in range(0,4):
mat_sig=sinal[ini:fim,:]
os.chdir('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT')
filename='SSVEP_15Hz_Trial9_'+str(k)+'.npy'
np.save(filename,mat_sig)
ini=ini+750
fim=fim+750
if compara_cod==1:
Xv=np.ones([4,n_trial*n_win+1])
R=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT/R.npy')
w=np.load('/home/pi/Desktop/BCI/online/scripts/Data_record/'\
+individuo+'/Data_FILT/w.npy')
for k in range(0,4):
Data_f_c=np.load('/home/pi/Desktop/BCI/online/scripts/'\
+individuo+'/Data_FILT/SSVEP_15Hz_Trial9_'+str(k)+'.npy')
L_cont=0
for e in range(0,n_elec): # Para cada eletrodo, fft calculada
sig=Data_f_c[:,e]
sig_FFT=np.abs(fft(sig))/max(np.abs((fft(sig))))
for fr in freq:
ind_fr=fr*(t_sinal)
ind_fr=int(ind_fr)
Xv[k,L_cont]=sig_FFT[ind_fr-1]
L_cont=L_cont+1
y=np.dot(Xv,np.transpose(w))