-
Notifications
You must be signed in to change notification settings - Fork 6
/
Epoc_GUI.py
236 lines (228 loc) · 8.77 KB
/
Epoc_GUI.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
# -*- coding: utf-8 -*-
"""
@author: Christodoulos Benetatos - xribene
"""
###############################################################################
from __future__ import division
from pyqtgraph.Qt import QtCore, QtGui
from emokit.emotiv import Emotiv
import matplotlib.pyplot as plt
from collections import deque
from scipy import signal,fft
from Queue import Queue
import time, threading
import pyqtgraph as pg
import numpy as np
import sys
###############################################################################
# Helper functions
def eeg_fft(y,Fs=128,show=False,limits=[0,30,0,20]) :
y=np.atleast_2d(y)
[C,N]=np.shape(y)
T=1/Fs
y_f = fft(y)
xf = np.linspace(0.0, 1.0/(2.0*T), N/2,endpoint=False)
xf=np.atleast_2d(xf)
xf=np.tile(xf,(C,1))
yf= (np.square(2.0/N *np.abs(y_f[:,0:N//2])))
if show:
plt.ion()
plt.plot(np.squeeze(xf), np.squeeze(yf))
plt.axis(limits)
plt.xticks(range(limits[1]))
plt.grid()
return yf,xf
def filtering(eeg,cut_off=[2,40],mode='band',order=4,show=False,limits=[0,30,0,20],ex=[111,111]):
eeg=np.atleast_2d(eeg)
[C,N]=np.shape(eeg)
tmp=np.zeros(shape=(np.shape(eeg)))
b, a = signal.butter(order, np.array((cut_off))/(Fs/2), btype = mode)
for i in xrange(C):
if (i==ex[0]) or (i==ex[1]):
tmp[i,:]=eeg[i,:]
continue
tmp[i,:]=signal.filtfilt(b, a, eeg[i,:] )
#print np.mean(tmp[i,:])
if show:
eeg_fft(np.squeeze(eeg),Fs,show,limits)
eeg_fft(np.squeeze(tmp),Fs,show,limits)
return tmp
def quality_color(av):
aa=av//20
aa=(aa<255)*aa+(aa>255)*255+(aa==255)*255
return (255-aa, aa, 0)
def pad(array_in, result):
# zero pad array so that shape(array_in)=shape(result)
[a,b]=np.shape(array_in)
[k,l]=np.shape(result)
start1=int((k-a)/2)
start2=int((l-b)/2)
result[start1:(start1+a),start2:(start2+b)]=array_in
return result
def next_pow(x):
return 1<<(x-1).bit_length()
##############################################################################
class ring_buffer(object):
def __init__(self,size):
self.size=size
self._buffered= deque([], self.size)
def write(self, value):
self._buffered.append(value)
def write_ex(self, value):
self._buffered.extend(value)
def show(self):
print(self._buffered)
def copy(self,overlap):
tmp=list(self._buffered)
return tmp[0:overlap]
def calls(self):
return self.write.calls
def list_ret(self):
a=list(self._buffered)
return a
class Plotter():
def __init__(self,electrodes,tw_sec,step,q1,flag1):
self.q1=q1
self.flag1=flag1
self.step=step
self.channels=len(electrodes.split(' '))
self.curve=[]
self.p=[]
self.scores=[]
self.now=0
self.now2=0
self.ptr1=0
self.Fs=128
self.N=tw_sec*self.Fs
# array with reference shape for zeropad in line 144
self.b=np.zeros(shape=(self.channels,next_pow(self.N)))
if show:
self.win = pg.GraphicsWindow()
self.win.setWindowTitle('Emotiv Epoc EEG Data')
for i in xrange(2*self.channels):
if i<self.channels:
self.p.append(self.win.addPlot(colspan=self.channels,title=electrodes.split(' ')[i]))
self.win.nextRow()
if i>(self.channels-1):
self.p.append(self.win.addPlot(colspan=1,title=electrodes.split(' ')[i-self.channels]+' PSD'))
data1 = np.random.normal(size=10)
for i in xrange(2*self.channels):
self.curve.append(self.p[i].plot(data1))
self.text_peak=[]
self.arrow=[]
self.text_qual=[]
for i in xrange(2*self.channels):
if i<self.channels:
#p[i].setXRange(0,1000, padding=0)
self.p[i].setYRange(-200, 200, padding=0)
tmp_text=pg.TextItem(anchor=(-0.4,1.6), fill=(0, 0, 255, 100))
self.text_qual.append(tmp_text)
self.p[i].addItem(tmp_text)
if i>(self.channels-1):
self.p[i].setXRange(0, 50, padding=0)
self.p[i].setYRange(0, 20, padding=0)
tmp_text=pg.TextItem(anchor=(0.5,2), fill=(0,0,255, 80))
tmp_arrow=pg.ArrowItem( angle=-90,brush=(0,0,255))
self.p[i].addItem(tmp_text)
self.p[i].addItem(tmp_arrow)
self.text_peak.append(tmp_text)
self.arrow.append(tmp_arrow)
self.set_timer()
def update_plots(self):
#print time.time()-self.now2
#self.now2=time.time()
y=np.array(self.q1.get(block=True, timeout=5)).transpose(1,0,2)
qual=y[:,:,1]
val_a=y[:,:,0]
val=filtering(val_a,cut_off=[2 ,30],mode='band')
val_pad=pad(val,self.b)
valf,xf=eeg_fft(val_pad,self.Fs,show=False,limits=[0,30,0,20])
self.ptr1 += self.step
if show:
for i in xrange(2*self.channels):
if i<self.channels:
av_qual=qual[i].mean()
qual_color=quality_color(av_qual)
self.curve[i].setData(val[i],pen=pg.mkPen(color=qual_color),width=2)
self.curve[i].setPos(self.ptr1, 0)
self.text_qual[i].setText('%0.1f' % av_qual)
self.text_qual[i].setColor(color=(0,255,255))
self.text_qual[i].setPos(self.ptr1,0)
if i>(self.channels-1):
self.curve[i].setData(xf[0],(valf[i-self.channels]))
self.text_peak[i-self.channels].setText('%0.3f' % xf[0][np.argmax(valf[i-self.channels])])
self.text_peak[i-self.channels].setColor(color=(0,255,255))
self.text_peak[i-self.channels].setPos(xf[0][np.argmax(valf[i-self.channels])], valf[i-self.channels].max())
self.arrow[i-self.channels].setPos(xf[0][np.argmax(valf[i-self.channels])], valf[i-self.channels].max())
def check_flag(self):
self.flag1.wait()
#self.now=time.time()
self.update_plots()
#print time.time()-self.now
pass
def set_timer(self):
self.timer = QtCore.QTimer()
self.timer.timeout.connect(self.check_flag)
self.timer.start(10)
# reads data from emotiv and sends them to Plotter every 'step/Fs' seconds,
# through q1
class Reader(threading.Thread):
def __init__(self,q1,flag1,step,tw,electrodes):
super(Reader, self).__init__()
self.Fs=128
self._stop = threading.Event()
self.q1=q1
self.flag1=flag1
self.step=step
self.tw=tw*self.Fs
self.electrodes=electrodes
def stop(self):
self._stop.set()
def stopped(self):
return self._stop.isSet()
def run(self):
O1_buff = ring_buffer(self.tw)
i=0
#old=0
#old2=0
with Emotiv(display_output=False, verbose=True, write=False) as headset:
try:
while not self._stop.isSet():
packet = headset.dequeue()
if packet is not None:
i=i+1
data=[]
for name in electrodes.split(' '):
data.append([packet.sensors[name]['value'],packet.sensors[name]['quality']])
O1_buff.write(data)
#print(time.time()-old)
#old=time.time()
pass
if i==self.step:
self.flag1.set()
self.q1.put(O1_buff.list_ret())
self.flag1.clear()
i=0
#print(time.time()-old2)
#old2=time.time()
except :
pass
##############################################################################
if __name__ == '__main__':
Fs=128
# O1 O2 P7 P8 AF3 F7 F3 FC5 T7 T8 FC6 F4 F8 AF4 X Y
electrodes='O1 O2' # choose which sensors to graph
tw_sec=2 # time window in which fft will be calculated
q1=Queue()
step=np.round(0.5*Fs) # how many new points will be graphed in every update
# or how many seconds (0.5) between update_plots repetetions
# or overlap between tw_sec windows
flag1=threading.Event()
thread1=Reader(q1,flag1,step,tw_sec,electrodes)
show=1
app = QtGui.QApplication(sys.argv)
s = Plotter(electrodes,tw_sec,step,q1,flag1)
thread1.start()
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
thread1.stop()