class FeatureExtractor(object):
'''
Controls the processing chain and fetches the values needed for the classificator
'''
def __init__(self, dataCollector):
self.inputQueue = Queue()
self.outputQueue = Queue()
self.extractQueue = Queue()
self.sigUtil = SignalUtil()
self.eegUtil = EEGUtil()
self.collector = dataCollector
self.collectorThread = threading.Thread(target=self.collector.collectData)
self.processor = DataProcessor(self.inputQueue, self.outputQueue)
self.processingThread = threading.Thread(target=self.processor.processData)
self.extract = True
def start(self):
'''setting data handler and starts collecting'''
print("%s: starting feature extractor" % self.__class__.__name__)
self.collector.setHandler(self.handleDataSet)
self.collectorThread.start()
self.processingThread.start()
while self.extract:
try:
procData = self.outputQueue.get(timeout=1)
self.extractFeatures(procData)
except Empty:
pass
def extractFeatures(self, data):
features = []
for _, sigData in data.iteritems():
theta = self.eegUtil.getThetaChannel(sigData["fft"])
features.extend(theta)
self.extractQueue.put(array(features))
def handleDataSet(self, data):
'''Add the given data to the processingQueue'''
self.inputQueue.put(data)
def close(self):
self.processor.close()
self.processingThread.join()
self.collector.close()
self.collectorThread.join()
print("%s: closing feature extractor" % self.__class__.__name__)
class TestEEGUtil(unittest.TestCase):
def setUp(self):
self.util = EEGUtil()
def test__getSliceParams(self):
for _, freqRange in self.util.channel_ranges.iteritems():
self.assertEqual(type(self.util._getSliceParam(freqRange)[0]), int)
def test_getChannels_short(self):
data = [1, 2, 3, 4, 5, 6, 7, 8]
channels = self.util.getChannels(data)
self.assertTrue(len(channels) == 5)
self.assertTrue(len(channels["delta"]) > 0)
self.assertTrue(len(channels["theta"]) > 0)
self.assertTrue(len(channels["alpha"]) == 0)
def test_getChannels(self):
fft = TEST_DATA_12000Hz
channels = self.util.getChannels(fft)
flattenChannels = np.hstack(channels.values())
self.assertTrue(all([x in fft for x in flattenChannels]))
self.assertTrue(len(flattenChannels) <= len(fft))
def test_getSingleChannels(self):
fft = TEST_DATA_12000Hz
channels = self.util.getChannels(fft)
delta = self.util.getDeltaChannel(fft)
# TODO delta range from 0.5 to 4Hz, actual range from 1 - 4Hz
self.assertEqual(len(delta), 3)
self.assertTrue(all([x in channels["delta"] for x in delta]))
theta = self.util.getThetaChannel(fft)
self.assertEqual(len(theta), 4)
self.assertTrue(all([x in channels["theta"] for x in theta]))
alpha = self.util.getAlphaChannel(fft)
self.assertEqual(len(alpha), 5)
self.assertTrue(all([x in channels["alpha"] for x in alpha]))
beta = self.util.getBetaChannel(fft)
self.assertEqual(len(beta), 17)
self.assertTrue(all([x in channels["beta"] for x in beta]))
gamma = self.util.getGammaChannel(fft)
self.assertEqual(len(gamma), len(range(*self.util.channel_ranges["gamma"])))
self.assertTrue(all([x in channels["gamma"] for x in gamma]))
def test_getWaves(self):
eegData = EEGTableFileUtil().readFile(PATH + "example_32.csv")
eeg = eegData.getColumn("F3")
nEeg = len(eeg)
waves = self.util.getWaves(eeg, eegData.getSamplingRate())
self.assertEqual(len(waves), 5)
for _, wave in waves.iteritems():
self.assertEqual(len(wave), nEeg)
def test_getSingleWaves(self):
eegData = EEGTableFileUtil().readFile(PATH + "example_32.csv")
eeg = eegData.getColumn("F3")
nEeg = len(eeg)
samplingRate = eegData.getSamplingRate()
waves = self.util.getWaves(eeg, samplingRate)
delta = self.util.getDeltaWaves(eeg, samplingRate)
self.assertEqual(len(delta), nEeg)
self.assertTrue(all([x in waves["delta"] for x in delta]))
theta = self.util.getThetaWaves(eeg, samplingRate)
self.assertEqual(len(theta), nEeg)
self.assertTrue(all([x in waves["theta"] for x in theta]))
alpha = self.util.getAlphaWaves(eeg, samplingRate)
self.assertEqual(len(alpha), nEeg)
self.assertTrue(all([x in waves["alpha"] for x in alpha]))
beta = self.util.getBetaWaves(eeg, samplingRate)
self.assertEqual(len(beta), nEeg)
self.assertTrue(all([x in waves["alpha"] for x in alpha]))
gamma = self.util.getGammaWaves(eeg, samplingRate)
self.assertEqual(len(gamma), nEeg)
self.assertTrue(all([x in waves["gamma"] for x in gamma]))
