def mindwaveArray(self):
output = {
"delta": deque(maxlen=30),
"theta": deque(maxlen=30),
"lowAlpha": deque(maxlen=30),
"highAlpha": deque(maxlen=30),
"lowBeta": deque(maxlen=30),
"highBeta": deque(maxlen=30),
"lowGamma": deque(maxlen=30),
"midGamma": deque(maxlen=30),
}
while not thread_stop_event.isSet():
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
if mindwaveDataPointReader.isConnected():
while True:
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if dataPoint.__class__ is EEGPowersDataPoint:
newData = dataPoint.__dict__
for k, v in newData.items():
if k in output.keys():
output[k].appendleft(v)
prepData = copy.deepcopy(output)
for k, v in prepData.items():
prepData[k] = list(v)
socketio.emit(
"newnumber",
{"output": json.dumps(prepData)},
namespace="/test",
)
else:
output = "Could not connect to the Mindwave Mobile device, retrying ..."
socketio.emit("newnumber", {"output": output}, namespace="/test")
sleep(self.delay)
def main():
configureLogger("WSO2IOT_RPiStats")
UtilsThread()
SubscribeToMQTTQueue(
) # connects and subscribes to an MQTT Queue that receives MQTT commands from the server
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
counter = 0
signalInfo = [0] * 11
while (True):
try:
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (not dataPoint.__class__ is RawDataPoint):
if (dataPoint.__class__ is EEGPowersDataPoint):
signalInfo[2] = float(str(dataPoint.delta)),
signalInfo[3] = dataPoint.theta,
signalInfo[4] = dataPoint.lowAlpha,
signalInfo[5] = dataPoint.highAlpha,
signalInfo[6] = dataPoint.lowBeta,
signalInfo[7] = dataPoint.highBeta,
signalInfo[8] = dataPoint.lowGamma,
signalInfo[9] = dataPoint.midGamma
if (dataPoint.__class__ is AttentionDataPoint):
signalInfo[0] = float(str(dataPoint).split(":")[1])
# TODO send command to virtual fire alarm after checking AttentionDataPointor or signalInfo[0]
try:
if (dataPoint.__class__ is PoorSignalLevelDataPoint):
signalInfo[1] = float(str(dataPoint).split(":")[1])
except ValueError:
print "200 - NO CONTACT TO SKIN"
signalInfo[1] = 200
pass
if (dataPoint.__class__ is MeditationDataPoint):
# TODO send command to virtual fire alarm after checking MeditationDataPoint or signalInfo[10]
signalInfo[10] = float(str(dataPoint).split(":")[1])
counter = counter + 1
if (counter == 4):
counter = 0
currentTime = calendar.timegm(time.gmtime())
PUSH_DATA_BRAIN_WAVE_INFO = iotUtils.BRAIN_WAVE_INFO.format(
currentTime, signalInfo[0], signalInfo[1],
signalInfo[10], float(signalInfo[2][0]),
float(signalInfo[3][0]), float(signalInfo[4][0]),
float(signalInfo[5][0]), float(signalInfo[6][0]),
float(signalInfo[7][0]), float(signalInfo[8][0]),
float(signalInfo[8][0]))
print '~~~~~~~~~~~~~~~~~~~~~~~~ Publishing Devic-eData ~~~~~~~~~~~~~~~~~~~~~~~~~'
print('PUBLISHED DATA: ' + PUSH_DATA_BRAIN_WAVE_INFO)
print('PUBLISHED TOPIC: ' +
mqttConnector.TOPIC_TO_PUBLISH_BRAIN_WAVE_INFO)
mqttConnector.publish(PUSH_DATA_BRAIN_WAVE_INFO)
except AssertionError:
print('An error occurred while reading brain signal')
pass
else:
print(
textwrap.dedent("""\
Exiting because the program could not connect
to the Mindwave Mobile device.""").replace("\n", " "))
def main():
rospy.init_node('mindwave')
pub = rospy.Publisher('neuro', MindwaveValues, queue_size=100)
rate = rospy.Rate(100)
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
while not rospy.is_shutdown():
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (dataPoint.__class__ is MeditationDataPoint):
print(dataPoint.__class__)
print(dataPoint)
dic = dataPoint.__dict__
meditation = dic['meditationValue']
print(meditation)
if (dataPoint.__class__ is AttentionDataPoint):
print(dataPoint.__class__)
print(dataPoint)
dic = dataPoint.__dict__
attention = dic['attentionValue']
print(attention)
send_values = MindwaveValues()
send_values.meditation = meditation
send_values.attention = attention
pub.publish(send_values)
rate.sleep()
else:
print(
textwrap.dedent("""\
Exiting because the program could not connect
to the Mindwave Mobile device.""").replace("\n", " "))
def __init__(self):
Source.__init__(self, self.get_dict().next)
self.logger = logging.getLogger('bws')
self.mindwaveDataPointReader = MindwaveDataPointReader()
self.mindwaveDataPointReader.start()
self.finished = False
self.initialized = False
self.started = False
def read(queue, mwm_pipeend, display_freq, episode_duration):
#used for data acquisition timing
counter = 0
record_times = []
#mwm collects data at approximately 512 Hz. Can vary though. That's why we need the trim.
trim_size = (episode_duration - .4) * 508
print("episode_duration:", episode_duration)
episode_data = []
#creates a new mindwave mobile object
mwm_reader = MindwaveDataPointReader()
mwm_reader.start()
#finds the start signal
while True:
data_point = mwm_reader.readNextDataPoint()
if data_point.__class__ is StartCommand:
print("Start sequence received, signaling animation...")
#once the start command is received from the mwm, the start signal is sent through the pipe to the animation process
mwm_pipeend.send([True])
break
epi_type = mwm_pipeend.recv()[0]
print("\tCommencing data acquisition at", time.time())
#for as long as the episode is happening, read data from mwm. For some reason, mwm closes too late. Need early stopping
#that's why I subtract .4 seconds from the end_time here
end_time = (time.time() + episode_duration - .4) - .1
while time.time() < end_time:
data_point = mwm_reader.readNextDataPoint()
if (data_point.__class__ is RawDataPoint):
episode_data.append(data_point._readRawValue())
if (display_freq):
current_time = time.time()
record_times.append(current_time)
counter += 1
if (counter == 240):
l = np.average(np.diff(record_times))
print("\t\tmwm:", 1 / l, "Hz")
record_times = []
counter = 0
print("\tClosing mwm stream at", time.time())
#get the episode type from the environment
# print("trimming ", len(episode_data) - int(trim_size), "readings")
# trim the episode data so it's always a constant length
print("trimming:", len(episode_data) - trim_size)
episode_data = episode_data[:int(trim_size)]
episode_data_df = pd.DataFrame().append([episode_data])
episode_data_df['episode_type'] = np.array([epi_type])
#close and delete mwm
mwm_reader.end()
del mwm_reader
#when the episode is finished, the data gathered by the recorder device is accessed and placed in the queue so the parent process can obtain it
queue.put(episode_data_df)
def connectMWM(self, target_address):
"""
make the MWM connection
"""
print "Connecting to MWM...",
try:
self.mindwaveDataPointReader = MindwaveDataPointReader(target_address)
self.mindwaveDataPointReader.start() # connect to the mindwave mobile headset
except IOError as e:
print e
print "Couldn't connect to MWM device. Check address? Is it turned on?";
raise e
print " connected"
def connect_EEG(self, addr):
self.print_log("[+] Try to connect")
self.mindwaveDataPointReader = MindwaveDataPointReader(addr)
while True:
try:
self.mindwaveDataPointReader.start()
time.sleep(2)
if (self.mindwaveDataPointReader.isConnected()):
self.print_log("[+] Connected")
self.EEG_status = True
return self.EEG_status
except:
self.print_log("[-] Fail Connection")
time.sleep(2)
self.print_log("[+] retry...")
def read(queue):
global results
mwm_reader = MindwaveDataPointReader()
mwm_reader.start()
recorder = rcrdr.Recorder(episode_duration=episode_duration,
mwm_reader=mwm_reader)
time_out_time = time.time() + 10
while time.time() < time_out_time:
data_point = mwm_reader.readNextDataPoint()
if data_point.__class__ is StartCommand:
print("start sequence received, commencing episode...")
break
recorder.record_one_episode(1, time.time())
print(recorder.get_episode_data())
queue.put(recorder.get_episode_data())
def read(trains):
raw = []
MindwaveData = MindwaveDataPointReader.MindwaveDataPointReader()
MindwaveData.start()
while(True):
dataPoint = MindwaveData.readNextDataPoint()
if (dataPoint.__class__ is RawDataPoint):
raw.append(float(dataPoint.rawValue))
for train in trains: ##把训练样本拉出来准备比较
fitRate = compare(train.data,raw).fitRate ##实例化并取得fitRate相关系数
if fitRate > 0.9:
print '%s.The fitRate is %s'%(train.name,fitRate)
print '\n'
else:
raw = []
def attempt_connect(eeg_addr):
# print('EEG Address in Attempt Connect: {}'.format(eeg_addr))
connect = False
# Create Mindwave objects
eeg_obj = MindwaveDataPointReader(eeg_addr)
# Attempt connection to EEG Object
try:
eeg_obj.start()
except IOError as e:
pass
# print('EEG Not Connected: {}'.format(eeg_addr))
else:
connect = True
print('EEG Connected: {}'.format(eeg_addr))
# return eeg_obj
return eeg_obj, connect
def main():
mdpr = MindwaveDataPointReader()
mdpr.start()
eeg_datapoints = []
attention_datapoints = []
index = 0
try:
while (True):
data = mdpr.readNextDataPoint()
if (data.__class__ is AttentionDataPoint):
attention_datapoints.append((time.time(), data))
if (data.__class__ is EEGPowersDataPoint):
eeg_datapoints.append((time.time(), data))
index += 1
print index
except KeyboardInterrupt:
pass
fmt = 'ddddddddd'
dataFormat = []
file_ = open(sys.argv[1], 'wb')
file_.write(fmt.ljust(25, ' '))
for i in xrange(len(eeg_datapoints)):
timestamp = attention_datapoints[i][0]
attention = attention_datapoints[i][1]
delta = eeg_datapoints[i][1].delta
theta = eeg_datapoints[i][1].theta
lowalpha = eeg_datapoints[i][1].lowAlpha
highalpha = eeg_datapoints[i][1].highAlpha
lowbeta = eeg_datapoints[i][1].lowBeta
highbeta = eeg_datapoints[i][1].highBeta
lowgamma = eeg_datapoints[i][1].lowGamma
midgamma = eeg_datapoints[i][1].midGamma
s = struct.pack(fmt, timestamp, delta, theta, lowalpha, highalpha,
lowbeta, highbeta, lowgamma, midgamma)
file_.write(s)
file_.close()
# some X and Y data
x = np.arange(100)
y = np.random.rand(100)
z = np.random.rand(100)
li1, = ax.plot(x, y, color="b", linewidth=2.0)
li2, = ax.plot(x, z, color="g")
# draw and show it
fig.canvas.draw()
plt.show(block=False)
# threshold value
threshold = 100
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
b = generate_bot()
if (mindwaveDataPointReader.isConnected()):
while (True):
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (not dataPoint.__class__ is RawDataPoint):
print dataPoint
# print(dataPoint.__class__)
if dataPoint.__class__.__name__ == "AttentionDataPoint":
# print(dataPoint.__class__)
if dataPoint.attentionValue > threshol:
print('Threshold exceeed!')
time.sleep(0.2) ## Wait
import bluetooth
import numpy as np
from mindwavemobile.MindwaveDataPoints import RawDataPoint
from mindwavemobile.MindwaveDataPointReader import MindwaveDataPointReader
with open("Output.txt", "w") as text_file: #輸出資料
if __name__ == '__main__':
mindwaveDataPointReader = MindwaveDataPointReader(
address='98:07:2D:7F:40:FF') #輸入你的腦波儀序號
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
while (True):
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (dataPoint.__class__.__name__ == 'AttentionDataPoint'):
x = np.array(dataPoint)
print(dataPoint)
print(f"{dataPoint}", file=text_file)
else:
pass
else:
print(
'Exiting because the program could not connect to the TGAM device.'
)
return dataLabel, deviceAddr
if __name__ == '__main__':
dataLabel, blueAddr = get_arguments()
fileName = time.strftime("%Y%m%d%H%M%S_") + dataLabel[0]
print('File Label:{}'.format(dataLabel))
print('Device Address:{}'.format(blueAddr))
with open(fileName + '.csv', mode='w') as logFileName:
log_writer = csv.writer(logFileName,
delimiter='\t',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
mindwaveDataPointReader = MindwaveDataPointReader(blueAddr)
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
while (True):
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (not dataPoint.__class__ is RawDataPoint):
data = str(dataPoint).split(':')
if (data[0] == 'delta'):
log_writer.writerow([time.strftime("%Y%m%d%H%M%S_")] +
data)
else:
print(dataPoint)
else:
print((textwrap.dedent("""\
Exiting because the program could not connect
to the Mindwave Mobile device.""").replace("\n", " ")))
import time
import bluetooth
from mindwavemobile.MindwaveDataPoints import RawDataPoint, AttentionDataPoint, MeditationDataPoint, PoorSignalLevelDataPoint
from mindwavemobile.MindwaveDataPointReader import MindwaveDataPointReader
import textwrap
if __name__ == '__main__':
#Try to connect to the predefined device
mindwaveDataPointReader = MindwaveDataPointReader('20:68:9D:3F:4F:88')
mindwaveDataPointReader.start()
#If the predefined device can't be connected, try any on discovery mode
if(not mindwaveDataPointReader.isConnected()):
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
else:
print('Connected to predefined device')
count = 0
if (mindwaveDataPointReader.isConnected()):
while(True):
'''dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (not dataPoint.__class__ is RawDataPoint):
print count,'-----------------------------------------------'
print dataPoint
print 'type', type(dataPoint), dataPoint.__class__
print '-----------------------------------------------'''
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if(dataPoint.__class__ is AttentionDataPoint):
print dataPoint
elif(dataPoint.__class__ is MeditationDataPoint):
print dataPoint
def read(trains):
raw = []
MindwaveData = MindwaveDataPointReader.MindwaveDataPointReader()
MindwaveData.start()
while(True):
dataPoint = MindwaveData.readNextDataPoint()
if (dataPoint.__class__ is RawDataPoint):
raw.append(float(dataPoint.rawValue))
for train in trains: ##把训练样本拉出来准备比较
fitRate = compare(train.data,raw).fitRate ##实例化并取得fitRate相关系数
if fitRate > 0.9:
print '%s.The fitRate is %s'%(train.name,fitRate)
print '\n'
else:
raw = []
if __name__ == '__main__':
mindwaveDataPointReader = MindwaveDataPointReader(address="AB:90:78:56:40:BD")
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
while(True):
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (not dataPoint.__class__ is RawDataPoint):
print dataPoint
else:
print(textwrap.dedent("""\
Exiting because the program could not connect
to the Mindwave Mobile device.""").replace("\n", " "))
trains = [train(data[54:56],'Blink1'),train(data[69:71],'Blink2'),train(data[70:73],'Relax')
def main():
#Default setting
macaddr = ""
spi_ch = 0
sensor_ch = 0
pNNx = 50
npNN = 30
nBPM = 10
#Load configuration file
try:
settingFile = open("setting.txt", "r")
print('Fond setting file')
tmp = settingFile.readlines()
settingFile.close()
for line in tmp:
line = line.strip()
if line[0] == '#' or line[0] == '':
continue
else:
cmd = line.split(':')
if cmd[0].lower() == 'macaddr':
macaddr = cmd[1].strip()
elif cmd[0].lower() == 'spi-ch':
spi_ch = int(cmd[1].strip())
elif cmd[0].lower() == 'pulse-ch':
sensor_ch = int(cmd[1].strip())
elif cmd[0].lower() == 'pnnx':
pNNx = int(cmd[1].strip())
elif cmd[0].lower() == 'n-pnnx':
npNN = int(cmd[1].strip())
elif cmd[0].lower() == 'n-bpm':
nBPM = int(cmd[1].strip())
else:
print('Unknown setting', cmd[0])
except:
print('File not found, use default setting')
newFlag = BOOL(False)
global threadRun
IBI = PULSEIBI(size=npNN, pNNx=pNNx)
queue = QUEUE()
rawpulse = RAWPULSE(length=3, diff=-0.002)
rawpulse_size = rawpulse.size()
ThreadStarted = 0
raw_emotion = np.zeros(
(8)
) #[happy,excite,surprise,strain,sad,ennui,calm,sleep] -> 'pleasure','excite','arouse','distress','misery','depress','sleep','content'
percent_emotion = [0] * 8
percent_min_emotion = [0] * 8
#Prepare for multiprocessing, plot graph
global ctype_threadRun
ctype_percent_emotion = multiprocessing.Array('d', [0] * 8)
ctype_oneMinEmo = multiprocessing.Array('d', [0] * 8)
ctype_rawpulse = multiprocessing.Array('i', [0] * rawpulse_size)
ctype_rawpulse_label = multiprocessing.Array('d', rawpulse.label())
bpm = IBI.BPM()
if bpm is None:
ctype_bpm = multiprocessing.Value('i', 0)
else:
ctype_bpm = multiprocessing.Value('i', bpm)
multi_cond = multiprocessing.Condition()
bv_condition = threading.Condition()
#Try to connect to the predefined device
print("Main program is started on PID", os.getpid())
try:
pl = threading.Thread(target=pulse.pulsePNN,
name="Pulse sensor reader",
args=(spi_ch, sensor_ch, threadRun, IBI,
rawpulse))
pl.daemon = True
pl.start()
ThreadStarted = 1
print("Reading pulse sensor thread is started")
except:
e = sys.exc_info()[0]
print("Can not start the pulse sensor reader thread", e)
threadRun.stop()
ctype_threadRun.value = 0
return
try:
pt = multiprocessing.Process(target=plotGraph,
name="Graph plotter",
args=(ctype_threadRun, multi_cond,
ctype_percent_emotion,
ctype_oneMinEmo, ctype_rawpulse,
ctype_rawpulse_label, ctype_bpm))
pt.daemon = True
pt.start()
ThreadStarted = 2
print("Plotting process is started")
#Tflag = True
except:
e = sys.exc_info()[0]
print("Can not start the plotting process", e)
threadRun.stop()
ctype_threadRun.value = 0
return
if macaddr != '':
mindwaveDataPointReader = MindwaveDataPointReader("20:68:9D:3F:4F:88")
mindwaveDataPointReader.start()
if (not mindwaveDataPointReader.isConnected()):
print("Find another device...")
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
else:
print("Searching for device")
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
print('Connected to', mindwaveDataPointReader.mac())
try:
mv = threading.Thread(target=getSignal,
name="mind wave reader",
args=(mindwaveDataPointReader, bv_condition,
threadRun, newFlag, queue, IBI))
mv.daemon = True
mv.start()
ThreadStarted = 3
print("Reading mind wave thread is started!")
#Tflag = True
except:
e = sys.exc_info()[0]
print("Can not start the mind wave reader thread", e)
threadRun.stop()
ctype_threadRun.value = 0
return
#while threadRun.status():
start = datetime.now()
oneMinEmo = CIR_ARRAY(60)
this_emotion = np.empty((8))
while threadRun:
#Critical section
with bv_condition:
#bv_condition.acquire()
while not newFlag:
bv_condition.wait()
if not threadRun:
break
#print queue.getAll()
qData = queue.getAll()
#[poorSignal,meditaton,attention,BPM,pNN50]
now = datetime.now()
newFlag.false()
#print('QLEN:',len(qData))
#print ('\n\tC-RELE')
#bv_condition.release()
#End of Critical section
#The computation unit starts here!
updateFlag = False
for data in qData:
if data[4] is not None and data[0] < 50:
arousal = data[1] - data[
2] #Equivalent to kakusei in Tanaka's work
updateFlag = True
if data[4] < 0.3:
pleasure = -100 * (data[4] / 0.3)
else:
pleasure = 100 * (data[4] - 0.3) / 0.7
angle = math.degrees(math.atan2(arousal, pleasure))
if angle < 0:
angle += 360
elif angle >= 360:
angle -= 360
if angle < 0 or angle > 360:
print(
'Unexpected error on angle, stopping for 10 seconds'
)
time.sleep(10)
# OLD 'happy','excite','surprise','strain','sad','ennui','calm','sleep'
# NEW 'pleasure','excite','arouse','distress','misery','depress','sleep','content'
#emotion classification
this_emotion[:] = 0
'''
if angle < 45:
#angle between 0 - 44
raw_emotion[0] += 1
raw_emotion[1] += 1
this_emotion[0] += 1
this_emotion[1] += 1
elif angle < 90:
#angle between 45 - 89
raw_emotion[2] += 1
raw_emotion[1] += 1
this_emotion[2] += 1
this_emotion[1] += 1
elif angle < 135:
#angle between 90 - 134
raw_emotion[2] += 1
raw_emotion[3] += 1
this_emotion[2] += 1
this_emotion[3] += 1
elif angle < 180:
#angle between 135 - 179
raw_emotion[4] += 1
raw_emotion[3] += 1
this_emotion[4] += 1
this_emotion[3] += 1
elif angle < 225:
#angle between 180 - 224
raw_emotion[4] += 1
raw_emotion[5] += 1
this_emotion[4] += 1
this_emotion[5] += 1
elif angle < 270:
#angle between 225 - 269
raw_emotion[7] += 1
raw_emotion[5] += 1
this_emotion[7] += 1
this_emotion[5] += 1
elif angle < 315:
#angle between 270 - 314
raw_emotion[7] += 1
raw_emotion[6] += 1
this_emotion[7] += 1
this_emotion[6] += 1
else:
#angle between 315 - 359
raw_emotion[0] += 1
raw_emotion[6] += 1
this_emotion[0] += 1
this_emotion[6] += 1
'''
if angle < 45:
#pleasure - excite
#angle between 0 - 44
emotionB = angle / 45.0
emotionA = 1 - emotionB
raw_emotion[0] += emotionA
raw_emotion[1] += emotionB
this_emotion[0] += emotionA
this_emotion[1] += emotionB
elif angle < 90:
#excite - arouse
#angle between 45 - 89
emotionB = (angle - 45.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[1] += emotionA
raw_emotion[2] += emotionB
this_emotion[1] += emotionA
this_emotion[2] += emotionB
elif angle < 135:
#arouse - distress
#angle between 90 - 134
emotionB = (angle - 90.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[2] += emotionA
raw_emotion[3] += emotionB
this_emotion[2] += emotionA
this_emotion[3] += emotionB
elif angle < 180:
#distress - misery
#angle between 135 - 179
emotionB = (angle - 135.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[3] += emotionA
raw_emotion[4] += emotionB
this_emotion[3] += emotionA
this_emotion[4] += emotionB
elif angle < 225:
#misery - depress
#angle between 180 - 224
emotionB = (angle - 180.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[4] += emotionA
raw_emotion[5] += emotionB
this_emotion[4] += emotionA
this_emotion[5] += emotionB
elif angle < 270:
#depress - sleep
#angle between 225 - 269
emotionB = (angle - 225.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[5] += emotionA
raw_emotion[6] += emotionB
this_emotion[5] += emotionA
this_emotion[6] += emotionB
elif angle < 315:
#sleep - content
#angle between 270 - 314
emotionB = (angle - 270.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[6] += emotionA
raw_emotion[7] += emotionB
this_emotion[6] += emotionA
this_emotion[7] += emotionB
else:
#content - pleasure
#angle between 315 - 360
emotionB = (angle - 315.0) / 45.0
emotionA = 1 - emotionB
raw_emotion[7] += emotionA
raw_emotion[0] += emotionB
this_emotion[7] += emotionA
this_emotion[0] += emotionB
print('emotion: ', raw_emotion, round(data[4], 2), angle)
oneMinEmo.put(this_emotion)
else:
oneMinEmo.put([0, 0, 0, 0, 0, 0, 0, 0])
#Updating the graph
if updateFlag:
#Calculate the graph
raw_sum = raw_emotion.sum()
if raw_sum != 0:
percent_emotion[:] = raw_emotion * (100 /
raw_emotion.sum())
else:
percent_emotion[:] = [0] * 8
#Calculate the one min graph
oneMin = oneMinEmo.get()
oneMin_raw = np.array([sum(x) for x in zip(*oneMin)])
oneMin_sum = sum(oneMin_raw)
if oneMin_sum != 0:
factor = 100.0 / oneMin_sum
percent_min_emotion[:] = oneMin_raw * (100.0 / oneMin_sum)
else:
percent_min_emotion[:] = [0] * 8
with multi_cond:
ctype_percent_emotion[:] = percent_emotion
ctype_oneMinEmo[:] = percent_min_emotion
ctype_rawpulse[:] = rawpulse.get()
bpm = IBI.BPM(nBPM)
if bpm is None:
ctype_bpm.value = 0
else:
ctype_bpm.value = int(round(bpm))
multi_cond.notify()
with multi_cond:
multi_cond.notify()
mindwaveDataPointReader.close()
if ThreadStarted == 1:
if pl.is_alive():
pl.join(5)
print("1 Child thread are successfully closed")
elif ThreadStarted == 2:
if pl.is_alive():
pl.join(5)
if pt.is_alive():
pt.join(5)
print("2 Child thread are successfully closed")
elif ThreadStarted == 3:
if pl.is_alive():
pl.join(5)
if mv.is_alive():
mv.join(5)
if pt.is_alive():
pt.join(10)
print("3 Child thread are successfully closed")
sys.exit(0)
import time
import bluetooth
from mindwavemobile.MindwaveDataPoints import RawDataPoint
from mindwavemobile.MindwaveDataPointReader import MindwaveDataPointReader
import textwrap
if __name__ == '__main__':
# mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader = MindwaveDataPointReader("CC:78:AB:15:40:93")
mindwaveDataPointReader.start()
if (mindwaveDataPointReader.isConnected()):
while (True):
try:
dataPoint = mindwaveDataPointReader.readNextDataPoint()
if (not dataPoint.__class__ is RawDataPoint):
print dataPoint
except IndexError:
pass
else:
print(
textwrap.dedent("""\
Exiting because the program could not connect
to the Mindwave Mobile device.""").replace("\n", " "))
def read(queue, mwm_pipeend, display_freq, episode_duration,
num_episodes): #num_episodes
counter = 0
record_times = []
current_episode = 0
trim_size = (episode_duration - .4) * 508 - 20
trial_data_df = pd.DataFrame()
# creates a new mindwave mobile object
mwm_reader = MindwaveDataPointReader()
mwm_reader.start()
# finds the start signal
while True:
data_point = mwm_reader.readNextDataPoint()
if data_point.__class__ is StartCommand:
print("Start sequence received.")
break
start_time = time.time()
#idles for 3 seconds to jump past annoying start spike
while (time.time() < start_time + 12):
mwm_reader.readNextDataPoint()
if (display_freq):
current_time = time.time()
record_times.append(current_time)
counter += 1
if (counter == 512):
l = np.average(np.diff(record_times))
print("\t\tmwm idling")
record_times = []
counter = 0
# once the start command is received from the mwm, the start signal is sent through the pipe to the animation process
mwm_pipeend.send(["start_trial"])
print("Startup spike passed. Trial begins.\n")
while (current_episode < num_episodes):
counter = 0
record_times = []
#used for data acquisition timing
episode_data = []
episode_reading_times = []
epi_type = mwm_pipeend.recv()[0]
mwm_reader.clearBuffer()
# mwm_reader.clearBufferV2()
mwm_pipeend.send(["buffer_cleared"])
#for as long as the episode is happening, read data from mwm. For some reason, mwm closes too late. Need early stopping
#that's why I subtract .4 seconds from the end_time here
end_time = (time.time() + episode_duration - .4) - .15
print("\tCommencing data acquisition at", time.time())
#record data for length of episode
while time.time() < end_time:
data_point = mwm_reader.readNextDataPoint()
if (data_point.__class__ is RawDataPoint):
episode_data.append(data_point._readRawValue())
episode_reading_times.append(time.time())
if (display_freq):
current_time = time.time()
record_times.append(current_time)
counter += 1
if (counter == 240):
l = np.average(np.diff(record_times))
print("\t\tmwm:", int(1 / l), "Hz")
record_times = []
counter = 0
print("\tEnding data acquisition at", time.time())
correct_index = 1024 #this value represents the index at which Pacman initially moves
#receives the time at which pacman makes his move from the environment
action_time = mwm_pipeend.recv()[0]
#finds the index closest in time to pacman's move time
action_index = episode_reading_times.index(
min(episode_reading_times, key=lambda x: abs(x - action_time)))
#adds dummy data to front of episode_data or removes offset number of readings so that the critical points are aligned
offset = action_index - correct_index
if (offset < 0):
dummy = [0] * abs(offset)
episode_data = dummy + episode_data
elif (offset > 0):
episode_data = episode_data[offset:]
to_trim = int(len(episode_data) - trim_size)
print("\t\ttrimming:", to_trim, "readings")
episode_data = episode_data[:int(trim_size)]
episode_data_df = pd.DataFrame().append([episode_data])
episode_data_df['episode_type'] = np.array([epi_type])
episode_data_df['episode_num'] = np.array([current_episode])
episode_data_df['action_index'] = np.array([action_index])
episode_data_df['trimmed_data'] = np.array([to_trim])
episode_data_df['win'] = np.array([epi_type in [0, 3]])
print("\tpredicting...")
prediction = predict(episode_data_df)
mwm_pipeend.send(prediction)
print("prediction sent")
#predict if episode data df is corrupt
if (action_index < 950 or action_index > 1100):
episode_data_df['is_corrupt'] = np.array([1])
elif (to_trim > 100 or to_trim < 0):
episode_data_df['is_corrupt'] = np.array([1])
else:
episode_data_df['is_corrupt'] = np.array([0])
# append to trial data df
trial_data_df = pd.concat([trial_data_df, episode_data_df])
print("Episode finished. \n\n")
#idle until animation sends finished animation signal
# while(not mwm_pipeend.recv()):
# mwm_reader.readNextDataPoint()
# if (display_freq):
# current_time = time.time()
# record_times.append(current_time)
# counter += 1
# if (counter == 240):
# l = np.average(np.diff(record_times))
# print("\t\tmwm:", 1 / l, "Hz")
# record_times = []
# counter = 0
current_episode += 1
#close and delete mwm
mwm_reader.end()
del mwm_reader
#when the episode is finished, the data gathered by the recorder device is accessed and placed in the queue so the parent process can obtain it
queue.put(trial_data_df)
def init():
global mindwaveDataPointReader
mindwaveDataPointReader = MindwaveDataPointReader()
mindwaveDataPointReader.start()
if playername == 'player1_val':
print "Player 1"
target_address = '/dev/tty.MindWaveMobile-DevA'
elif playername == 'player2_val':
target_address = '/dev/tty.MindWaveMobile-DevA-2'
#target_address = "/dev/tty.MindWaveMobile-DevA"
if not target_address:
raise IOError("No valid serial port address")
#return
print "Serial port address: " + target_address
print "Running " + playername
print "Connecting to MWM...",
try:
mindwaveDataPointReader = MindwaveDataPointReader(target_address)
mindwaveDataPointReader.start() # connect to the mindwave mobile headset
except IOError as e:
print e
print "Couldn't connect to MWM device. Check address? Is it turned on?";
raise e
#return
print " connected"
# datastream = collections.deque;
rawdatastream = deque()
attentiondata = deque()
meditationdata = deque()
bwdata = deque()
# Delta (0.5 - 2.75Hz), theta (3.5 - 6.75Hz), low-alpha (7.5 - 9.25Hz),
def main():
mdpr = MindwaveDataPointReader()
mdpr.start()
eeg_datapoints = []
attention_datapoints = []
try:
while (True):
data = mdpr.readNextDataPoint()
if (data.__class__ is AttentionDataPoint):
attention_datapoints.append(data)
if (data.__class__ is EEGPowersDataPoint):
eeg_datapoints.append(data)
except KeyboardInterrupt:
pass
# plot data
print len(eeg_datapoints)
print len(attention_datapoints)
attention_datapoint_vals = [
point.attentionValue for point in attention_datapoints
]
# will the following give us a good speed increase over using list comprehensions?
# or will the readability be better?
delta_points = []
theta_points = []
lowAlpha_points = []
highAlpha_points = []
lowBeta_points = []
highBeta_points = []
lowGamma_points = []
midGamma_points = []
for datapoint in eeg_datapoints:
# print(datapoint)
delta_points.append(datapoint.delta)
theta_points.append(datapoint.theta)
lowAlpha_points.append(datapoint.lowAlpha)
highAlpha_points.append(datapoint.highAlpha)
lowBeta_points.append(datapoint.lowBeta)
highBeta_points.append(datapoint.highBeta)
lowGamma_points.append(datapoint.lowGamma)
midGamma_points.append(datapoint.midGamma)
# two plots will make the data look more presentable since the scales
# of the data vary widely (maybe even more, like a plot per greek letter
# or something)
# plot the EEG components (scale: 0 - large)
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(delta_points)) for i in delta_points],
label="Delta")
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(theta_points)) for i in theta_points],
label="Theta")
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(lowAlpha_points)) for i in lowAlpha_points],
label="Low-Alpha")
pl.plot(
range(len(eeg_datapoints)),
[float(i) / float(sum(highAlpha_points)) for i in highAlpha_points],
label="High-Alpha")
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(lowBeta_points)) for i in lowBeta_points],
label="Low-Beta")
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(highBeta_points)) for i in highBeta_points],
label="High-Beta")
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(lowGamma_points)) for i in lowGamma_points],
label="Low-Gamma")
pl.plot(range(len(eeg_datapoints)),
[float(i) / float(sum(midGamma_points)) for i in midGamma_points],
label="Mid-Gamma")
pl.xlabel("Time Step")
pl.ylabel("Reading Value")
legend = pl.legend(loc='best', ncol=2, shadow=None)
legend.get_frame().set_facecolor('#00FFCC')
# pl.yscale('log')
pl.show()
# plot the attention reading (scale: 1- 1001)
pl.plot(range(len(attention_datapoints)),
attention_datapoint_vals,
label="Attention")
pl.xlabel("Time Step")
pl.ylabel("Reading Value")
legend = pl.legend(loc='best', ncol=2, shadow=None)
legend.get_frame().set_facecolor('#00FFCC')
pl.show()
