def __init__(self, verbose=False):
self.samplingRate = ConfigProvider().getEmotivConfig().get(
"samplingRate")
self.qualUtil = QualityUtil()
self.fftUtil = FFTUtil()
self.eegUtil = EEGUtil()
self.verbose = verbose
def plotEEGSignal(self):
# Filter a noisy signal.
data = self.getEEGSignal()
fs = data.getSamplingRate()
x = data.getColumn("F3")
x = SignalUtil().normalize(x)
duration = self.getDuration(data)
t = np.linspace(0, duration, len(x), endpoint=False)
plt.figure(2)
plt.clf()
plt.plot(t, x, label='normalized signal')
for label, (lowcut, highcut) in EEGUtil().channel_ranges.iteritems():
if label == "alpha":
y = self.su.butterBandpassFilter(x,
lowcut,
highcut,
fs,
order=6)
plt.plot(t,
y,
label='%s (%d - %dHz)' % (label, lowcut, highcut))
plt.xlabel('time (seconds)')
plt.grid(True)
plt.axis('tight')
plt.legend(loc='upper left')
class DeltaSignalPlotter(RawSignalPlotter):
def __init__(self,
person,
eegData,
signals,
filePath,
save=True,
plot=True,
logScale=False):
RawSignalPlotter.__init__(self,
person,
eegData,
signals,
filePath,
save,
plot,
logScale,
name="delta")
self.chain = SignalProcessor()
self.eegUtil = EEGUtil()
def _getData(self, signal):
raw = self.eegData.getColumn(signal)
qual = self.eegData.getQuality(signal)
samplingRate = self.eegData.getSamplingRate()
proc, _ = self.chain.process(raw, qual)
proc = QualityUtil().replaceNans(proc)
return self.eegUtil.getDeltaWaves(proc, samplingRate)
def __init__(self,
person,
eegData,
signals,
filePath,
save=True,
plot=True,
logScale=False):
RawSignalPlotter.__init__(self,
person,
eegData,
signals,
filePath,
save,
plot,
logScale,
name="delta")
self.chain = SignalProcessor()
self.eegUtil = EEGUtil()
class FFTProcessor(object):
def __init__(self, verbose=False):
self.samplingRate = ConfigProvider().getEmotivConfig().get(
"samplingRate")
self.qualUtil = QualityUtil()
self.fftUtil = FFTUtil()
self.eegUtil = EEGUtil()
self.verbose = verbose
def process(self, proc):
fft = self.fftUtil.fft(proc)
chan = self.eegUtil.getChannels(fft)
return chan, False
def __init__(self, queue, filePath, signals=None, save=True, plot=True, logScale=False, name=""):
self.queue = queue
self.filePath = filePath
self._initStatsDict()
self.config = ConfigProvider()
self.eegData = FileUtil().getDto(filePath)
self._initSignals(signals)
self.su = SignalUtil()
self.qu = QualityUtil()
self.eu = EEGUtil()
self.fft = FFTUtil()
self._initFields()
self.save = save
self.plot = plot
self.name = name
self._initPlotter(logScale)
self.ssPrint = SignalStatisticPrinter(filePath)
self.preProcessor = SignalPreProcessor()
self.processor = SignalProcessor()
windowSeconds = self.config.getCollectorConfig().get("windowSeconds")
self.windowSize = EEGDataCollector.calcWindowSize(windowSeconds, self.eegData.samplingRate)
def setUp(self):
self.util = EEGUtil()
class TestEEGUtil(BaseTest):
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 = self.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 = self.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 = FileUtil().getDto(self.getData32CSV())
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 = FileUtil().getDto(self.getData32CSV())
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]))