def FeatureExtraction(Sample,Feature,Data_Array):
# Append the data
Data_Array = np.append(Data_Array,Sample,axis=0)
# Common Average Reference
Sample[:,range(1,9)] = Process.Common_Average_Reference(Sample[:,range(1,9)])
# Feature Extraction
if ALPHA:
Feature = np.append(Feature,Process.AlphaDifference(Sample,([5,6],[7,8]),250))
else:
Feature = np.append(Feature,Process.PowerExtraction(Sample[:,3],[8,12],250))
return (Feature,Data_Array)
EEG_Recording = Sample
else:
EEG_Recording = np.append(EEG_Recording, Sample, axis=0)
# Common Average Reference
Sample[:, range(1, 9)] = Process.Common_Average_Reference(
Sample[:, range(1, 9)])
# Feature Extraction
if ALPHA:
Feature = np.append(
Feature, Process.AlphaDifference(Sample, ([5, 6], [7, 8]),
250))
else:
Feature = np.append(
Feature, Process.PowerExtraction(Sample[:, 3], [8, 12], 250))
FIFO.Rewrite(Trigger_Log, "Calibration On")
Trigger = np.array([[len(Feature), CALIBRATION_START]])
print "Calibration Stage 1"
Current_Index = len(Feature)
"""""" """""" """""" """""" """""" """""" """""" """
Resting State. Movement Range Calculation.
""" """""" """""" """""" """""" """""" """""" """"""
while len(Feature) - Current_Index < 5 * TaskSetting['Calibration']:
if not Data_Queue.empty():
Sample = Data_Queue.get()
Feature, EEG_Recording = FeatureExtraction(Sample, Feature,
EEG_Recording)
Generator.start()
#Board = bci.OpenBCIBoard_Recording(thread=True,port=port,baud=115200,Queue=Data_Queue,binSize=125)
#Board.start()
screenWidth, screenHeight = gui.size()
gui.FAILSAFE = False
MovementRange_X = [-5.0 * (screenWidth / 100.0), 5.0 * (screenWidth / 100.0)]
MovementRange_Y = [-5.0 * (screenHeight / 100.0), 5.0 * (screenHeight / 100.0)]
while True:
if not Data_Queue.empty():
Sample = Data_Queue.get()
Sample[:, range(1, 9)] = Process.Common_Average_Reference(
Sample[:, range(1, 9)])
Feature1.append(
Process.PowerExtraction(Sample[:, 4], FS, Frequency_Range))
Feature2.append(
Process.PowerExtraction(Sample[:, 7], FS, Frequency_Range))
Classifier_X, Offset_X = Process.Linear_Regression(
Feature1, MovementRange_X)
Classifier_Y, Offset_Y = Process.Linear_Regression(
Feature2, MovementRange_Y)
if not Classifier_X == np.inf and not Classifier_Y == np.inf:
break
while len(Feature1) < 70:
if not Data_Queue.empty():
Sample = Data_Queue.get()
Sample[:, range(1, 9)] = Process.Common_Average_Reference(
Sample[:, range(1, 9)])
Feature1.append(
