public void Start()
{
_queueStopped = false;
Task.Run(async() => {
bool queueEmpty = false;
int queueLastLength = 0;
int queueLengthSlope = 0;
while (!(queueEmpty && _queueStopped))
{
List <BciData> sample;
queueEmpty = !_queue.TryDequeue(out sample);
if (!queueEmpty)
{
Debug.WriteLine("Processing queue...");
// Update error and d_error
queueLengthSlope = _queue.Count - queueLastLength;
queueLastLength = _queue.Count;
AdjustParameters(queueLastLength, queueLengthSlope);
// Analyse data and fire events
double[] xValues = new double[sample.Count];
for (int i = 0; i < sample.Count; ++i)
{
xValues[i] = i;
}
for (int channel = 0; channel < 8; ++channel)
{
double[] yValues = new double[sample.Count];
for (int i = 0; i < sample.Count; ++i)
{
yValues[i] = sample[i].ChannelData[channel] * ScaleFactor;
}
IImfDecomposition decomp = await Emd.EnsembleDecomposeAsync(xValues, yValues, 1000, _ensembleCount);
//var decomp = await Emd.DecomposeAsync(xValues, yValues);
IHilbertSpectrum spectrum = (decomp.ImfFunctions.Count == 0) ?
null : await Hsa.GetHilbertSpectrumAsync(decomp, 1.0);
SampleAnalysed?.Invoke(spectrum, yValues, channel);
}
}
else
{
await Task.Delay(100);
}
}
});
}
public async Task Initialize(int channelIndex)
{
BciData[] sampleData = DataManager.Current.LastSample;
var channelData = new double[sampleData.Length];
for (int i = 0; i < sampleData.Length; ++i)
{
channelData[i] = sampleData[i].ChannelData[channelIndex] * DataManager.ScaleFactor;
}
double[] xValues = new double[channelData.Length];
for (int i = 0; i < channelData.Length; ++i)
{
xValues[i] = i;
}
Status = "Decomposing...";
IImfDecomposition decomp = await Emd.EnsembleDecomposeAsync(xValues, channelData,
1000, DataManager.Current.EnsembleCount);
Status = "Analysing...";
IHilbertSpectrum hs = await Hsa.GetHilbertSpectrumAsync(decomp, 1.0);
Status = null;
_xStep = hs.MaxFrequency / XLength;
var marginalData = new ChartValues <double>();
for (double w = 0; w <= hs.MaxFrequency; w += _xStep)
{
marginalData.Add(hs.ComputeMarginalAt(w));
}
double marginalMin = marginalData[0], marginalMax = marginalData[0], marginalMean = 0.0;
foreach (double val in marginalData)
{
marginalMean += val;
if (val < marginalMin)
{
marginalMin = val;
}
else if (val > marginalMax)
{
marginalMax = val;
}
}
marginalMean /= marginalData.Count;
Info = $"Channel = {channelIndex+1}\nMin value = {marginalMin}\nMax value = {marginalMax}\nAverage value = {marginalMean}";
var mapper = new CartesianMapper <double>()
.X((value, index) => hs.MinFrequency + index * _xStep)
.Y((value, index) => value);
_marginalSpectrum = new SeriesCollection(mapper)
{
new LineSeries {
Title = "Marginal Hilbert Spectrum",
Values = marginalData,
PointGeometry = DefaultGeometries.None,
StrokeThickness = 0.5,
Stroke = new SolidColorBrush(Colors.Red)
}
};
OnPropertyChanged(nameof(MarginalSpectrum));
}
private static List <double> PreProcess(List <double[]> signalsList, double Q, int m, double r, int N, int iterations, int locality)
{
double[] f3 = new double[signalsList.Count];
bool first = true;
for (int i = 0; i < f3.Length; i++)
{
if (first)
{
//f3[i] = BetaBandpass(signalsList[i][0], true);
f3[i] = signalsList[i][0];
first = false;
}
else
{
//f3[i] = BetaBandpass(signalsList[i][0], false);
f3[i] = signalsList[i][0];
}
}
string internalDirectory = _dataAccessFacade.GetGeneralSettings().GetModalDirectory("Emotion");
var file_F3 = File.AppendText(Path.Combine(internalDirectory, "F3.json"));
file_F3.WriteLine(f3.ToJsonString());
file_F3.Flush();
file_F3.Close();
//f3 = FilterRLC.LCHP(f3, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 5, Q);
//f3 = FilterRLC.LCLP(f3, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 50, Q);
for (int i = 0; i < f3.Length; i++)
{
if (double.IsInfinity(f3[i]) || double.IsNaN(f3[i]))
{
Console.WriteLine(i + " f3 es " + f3[i]);
f3[i] = f3[i - 1] * (1 + (_xrand.Next(-1, 1) / 10));
}
}
var file_F3Filtered = File.AppendText(Path.Combine(internalDirectory, "F3Filtered.json"));
file_F3Filtered.WriteLine(f3.ToJsonString());
file_F3Filtered.Flush();
file_F3Filtered.Close();
double[] c4 = new double[signalsList.Count];
first = true;
for (int i = 0; i < c4.Length; i++)
{
if (first)
{
//c4[i] = BetaBandpass(signalsList[i][1], true);
c4[i] = signalsList[i][1];
first = false;
}
else
{
//c4[i] = BetaBandpass(signalsList[i][1], false);
c4[i] = signalsList[i][1];
}
}
var file_C4 = File.AppendText(Path.Combine(internalDirectory, "C4.json"));
file_C4.WriteLine(c4.ToJsonString());
file_C4.Flush();
file_C4.Close();
//c4 = FilterRLC.LCHP(c4, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 5, Q);
//c4 = FilterRLC.LCLP(c4, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 50, Q);
for (int i = 0; i < c4.Length; i++)
{
if (double.IsInfinity(c4[i]) || double.IsNaN(c4[i]))
{
Console.WriteLine(i + " c4 es " + c4[i]);
c4[i] = c4[i - 1] * (1 + (_xrand.Next(-1, 1) / 10));
}
}
var file_C4Filtered = File.AppendText(Path.Combine(internalDirectory, "C4Filtered.json"));
file_C4Filtered.WriteLine(c4.ToJsonString());
file_C4Filtered.Flush();
file_C4Filtered.Close();
var emdF3 = new Emd();
var imfsF3 = emdF3.GetImfs(f3, 4, iterations, locality);
Console.WriteLine("obtenido imfs F3");
var emdC4 = new Emd();
var imfsC4 = emdC4.GetImfs(c4, 4, iterations, locality);
Console.WriteLine("obtenido imfs C4");
var file_imfsF3 = File.AppendText(Path.Combine(internalDirectory, "imfsF3.json"));
file_imfsF3.WriteLine(imfsF3.ToJsonString());
file_imfsF3.Flush();
file_imfsF3.Close();
var file_imfsC4 = File.AppendText(Path.Combine(internalDirectory, "imfsC4.json"));
file_imfsC4.WriteLine(imfsC4.ToJsonString());
file_imfsC4.Flush();
file_imfsC4.Close();
//return MH(imfsF3, imfsC4);
return(CalcEntropy(imfsF3, imfsC4, N, m, r));
}
private List <double> PreProcess(List <double[]> signalsList, double Q, int m, double r, int N, int iterations, int locality)
{
double[] f3 = new double[signalsList.Count];
bool first = true;
for (int i = 0; i < f3.Length; i++)
{
if (first)
{
f3[i] = BetaBandpass(signalsList[i][0], true);
//f3[i] = signalsList[i][0];
first = false;
}
else
{
f3[i] = BetaBandpass(signalsList[i][0], false);
//f3[i] = signalsList[i][0];
}
}
//f3 = FilterRLC.LCHP(f3, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 5, Q);
//f3 = FilterRLC.LCLP(f3, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 50, Q);
for (int i = 0; i < f3.Length; i++)
{
if (double.IsInfinity(f3[i]) || double.IsNaN(f3[i]))
{
Console.WriteLine("F3 es NaN o Infinityyyyyy");
f3[i] = f3[i - 1] * (1 + (_xrand.Next(-1, 1) / 10));
}
}
double[] c4 = new double[signalsList.Count];
first = true;
for (int i = 0; i < c4.Length; i++)
{
if (first)
{
c4[i] = BetaBandpass(signalsList[i][1], true);
//c4[i] = signalsList[i][1];
first = false;
}
else
{
c4[i] = BetaBandpass(signalsList[i][1], false);
//c4[i] = signalsList[i][1];
}
}
//c4 = FilterRLC.LCHP(c4, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 5, Q);
//c4 = FilterRLC.LCLP(c4, EEGEmoProc2ChSettings.Instance.SamplingHz.Value, 50, Q);
for (int i = 0; i < c4.Length; i++)
{
if (double.IsInfinity(c4[i]) || double.IsNaN(c4[i]))
{
Console.WriteLine("C4 es NaN o Infinityyyyyy");
c4[i] = c4[i - 1] * (1 + (_xrand.Next(-1, 1) / 10));
}
}
var emdF3 = new Emd();
var imfsF3 = emdF3.GetImfs(f3, 4, iterations, locality);
var emdC4 = new Emd();
var imfsC4 = emdC4.GetImfs(c4, 4, iterations, locality);
//return MH(imfsF3, imfsC4);
return(CalcEntropy(imfsF3, imfsC4, N, m, r));
}
