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)
SamplingData = Random_Data['GenericData']
Board = gen.Generic_Generator(Queue=Data_Queue,
binSize=50,
Data=SamplingData)
Board.start()
while len(Feature) < 5 * TaskSetting['Initiation']:
if not Data_Queue.empty():
Sample = Data_Queue.get()
if not EEG_Recording.any():
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)