def update():
global curve, waves, wavesx, wavesy, y, x, xT, ri, r, EEG_n, counter, instant, headset, time, fs, win_size
EEG_new = br.readEEG2(fs, time, channels, headset)
EEG_n_new = br.normalizeEEG(EEG_new)
y2 = EEG_n_new.transpose()
#--------------------------------------------------------
#Rhythms Bar Graphic
if EEG_n.shape[0] < w - 1:
EEG_n = np.r_[EEG_n, EEG_n_new]
else:
EEG_n = np.r_[EEG_n, EEG_n_new]
EEG_n = EEG_n[-w:, :]
ri = br.rhythmsAllWaves(fs, dx, EEG_n)
#ri = br.rhythmsBasicWaves(fs,dx,EEG_n)
r = br.normalizeRhythms(ri)
for i in range(len(r)):
graph["%s" % (str(i))].setOpts(x=[i], height=[r[i]])
#--------------------------------------------------------
#Waves Diagram
xr_new, yr_new = br.wavesDiagram(r, Deg)
if instant < 3:
waves.append(
plt2.plot(xr_new,
yr_new,
pen=(255, 255, 102),
fillLevel=0,
fillBrush=(102, 255, 102, 40)))
#plt2.plot(x_p, y_p, pen=None, symbol='o', symbolPen=(51,153,255), symbolBrush=(153,255,204))
wavesx.append(xr_new)
wavesy.append(yr_new)
instant += 1
else:
wavesx.append(xr_new)
wavesy.append(yr_new)
for k in range(1, len(waves)):
waves[k].setData(wavesx[k], wavesy[k])
wavesx = wavesx[-len(waves):]
wavesy = wavesy[-len(waves):]
#--------------------------------------------------------
#EEG Plot
if np.ceil(counter) < win_size:
y = np.c_[y, y2]
x = xT[:y.shape[1]]
else:
y = np.c_[y, y2]
y = y[:, int(time * fs):]
for i in range(channels):
curve[i].setData(x, y[i])
counter = counter + time
def update():
global curve, waves, wavesx, wavesy, y, x, xT, ri, r, EEG_n, counter,instant, headset, time,fs, win_size
EEG_new = br.readEEG2(fs,time,channels,headset)
EEG_n_new = br.normalizeEEG(EEG_new )
y2 = EEG_n_new.transpose()
#--------------------------------------------------------
#Rhythms Bar Graphic
if EEG_n.shape[0] < w-1:
EEG_n = np.r_[EEG_n,EEG_n_new]
else:
EEG_n = np.r_[EEG_n,EEG_n_new]
EEG_n = EEG_n[-w:,:]
ri = br.rhythmsAllWaves(fs,dx,EEG_n)
#ri = br.rhythmsBasicWaves(fs,dx,EEG_n)
r = br.normalizeRhythms(ri)
for i in range(len(r)):
graph["%s" %(str(i))].setOpts(x=[i], height=[r[i]])
#--------------------------------------------------------
#Waves Diagram
xr_new,yr_new = br.wavesDiagram(r,Deg)
if instant < 3:
waves.append(plt2.plot(xr_new, yr_new, pen=(255,255,102), fillLevel=0, fillBrush=(102,255,102,40)))
#plt2.plot(x_p, y_p, pen=None, symbol='o', symbolPen=(51,153,255), symbolBrush=(153,255,204))
wavesx.append(xr_new)
wavesy.append(yr_new)
instant += 1
else:
wavesx.append(xr_new)
wavesy.append(yr_new)
for k in range(1,len(waves)):
waves[k].setData(wavesx[k],wavesy[k])
wavesx = wavesx[-len(waves):]
wavesy = wavesy[-len(waves):]
#--------------------------------------------------------
#EEG Plot
if np.ceil(counter) < win_size:
y = np.c_[y,y2]
x = xT[:y.shape[1]]
else:
y = np.c_[y,y2]
y = y[:,int(time*fs):]
for i in range(channels):
curve[i].setData(x,y[i])
counter = counter + time
channels = 14
win_size = 5
color = [(255, 102, 102), (255, 178, 102), (255, 255, 102), (178, 255, 102),
(102, 255, 102), (102, 255, 178), (102, 255, 255), (102, 178, 255)]
#Deg = [90., 45., 330., 300., 240., 210., 150., 120.]
Deg = [90., 45., 0., 315., 270., 225., 180., 135.]
f_min = 0.8
w = (1 / f_min) * fs
EEG, headset = br.readEEG(fs, time, channels)
EEG_n = br.normalizeEEG(EEG)
y = EEG_n.transpose()
x = np.linspace(0, time, fs * time)
ri = br.rhythmsAllWaves(fs, dx, EEG)
#ri = br.rhythmsBasicWaves(fs,dx,EEG)
r = br.normalizeRhythms(ri)
xr, yr = br.wavesDiagram(r, Deg)
#Main Window
win = pg.GraphicsWindow(title="EEG Data System")
win.resize(1000, 600)
win.setWindowTitle('EEG Data System')
# Enable antialiasing for prettier plots
pg.setConfigOptions(antialias=True)
#--------------------------------------------------------
time = 0.4
channels = 14
win_size = 5
color = [(255,102,102),(255,178,102),(255, 255, 102),(178, 255, 102),(102, 255, 102),(102, 255, 178),(102,255,255),(102,178,255)]
#Deg = [90., 45., 330., 300., 240., 210., 150., 120.]
Deg = [90., 45., 0. ,315., 270. , 225., 180., 135.]
f_min = 0.8
w = (1/f_min)*fs
EEG,headset = br.readEEG(fs,time,channels)
EEG_n = br.normalizeEEG(EEG)
y = EEG_n.transpose()
x = np.linspace(0,time,fs*time)
ri = br.rhythmsAllWaves(fs,dx,EEG)
#ri = br.rhythmsBasicWaves(fs,dx,EEG)
r = br.normalizeRhythms(ri)
xr,yr = br.wavesDiagram(r,Deg)
#Main Window
win = pg.GraphicsWindow(title="EEG Data System")
win.resize(1000,600)
win.setWindowTitle('EEG Data System')
# Enable antialiasing for prettier plots
pg.setConfigOptions(antialias=True)
#--------------------------------------------------------
