public void CalculateFeature()
{
for (int i = 0; i < model.dataTraining.Count; i++)
{
model.dataTrainingFreq.Add(fft.DoWork(model.dataTraining[i]));
}
List <double[]> deltaTheta = new List <double[]>();
List <double[]> alpha = new List <double[]>();
List <double[]> beta = new List <double[]>();
List <double[]> gamma = new List <double[]>();
List <double[]> result;
for (int i = 0; i < model.dataTrainingFreq.Count; i++)
{
result = dsp.Decomposes(model.dataTrainingFreq[i]);
deltaTheta.Add(result[0]);
alpha.Add(result[1]);
beta.Add(result[2]);
gamma.Add(result[3]);
}
model.spectral.Add("deltaTheta", deltaTheta);
model.spectral.Add("alpha", alpha);
model.spectral.Add("beta", beta);
model.spectral.Add("gamma", gamma);
int jumlahSensor = model.GetTotalSelectedChannels();
int jumlahData = model.GetTotalData();
//MessageBox.Show(jumlahData.ToString() + " " + jumlahSensor.ToString());
List <double> fitur = new List <double>();;
for (int k = 0; k < jumlahData; k++)
{
for (int i = 0; i < jumlahSensor; i++)
{
fitur.Add(model.spectral["deltaTheta"][k][i]);
fitur.Add(model.spectral["alpha"][k][i]);
fitur.Add(model.spectral["beta"][k][i]);
fitur.Add(model.spectral["gamma"][k][i]);
}
if (jumlahSensor > 1)
{
for (int i = 0; i < jumlahSensor; i++)
{
for (int j = i + 1; j < jumlahSensor; j++)
{
double val1 = model.spectral["deltaTheta"][k][i];
double val2 = model.spectral["deltaTheta"][k][j];
double powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = model.spectral["alpha"][k][i];
val2 = model.spectral["alpha"][k][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = model.spectral["beta"][k][i];
val2 = model.spectral["beta"][k][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = model.spectral["gamma"][k][i];
val2 = model.spectral["gamma"][k][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
}
}
}
if ((k + 1) % bufferDecision == 0)
{
//MessageBox.Show(fitur.Count.ToString());
model.features.Add(fitur);
fitur = new List <double>();
}
}
model.SetFeatureClass();
//MessageBox.Show(model.features.Count.ToString());
}
private void timer_Tick(object sender, EventArgs e)
{
int totalSensor = models[comboBoxModel.SelectedIndex].GetTotalSelectedChannels();
dataTesting = new double[totalSensor];
if (checkShowChart.Checked)
{
for (int i = 0; i < totalChannels; i++)
{
DataPoint dp = new DataPoint();
dp.SetValueY(eegData[channelsEeg[i]][counter]);
chartEeg.Series[i].Points.Add(dp);
chartEeg.Update();
if (counter > LIMIT_WINDOW_CHART)
{
chartEeg.Series[i].Points.RemoveAt(0);
}
}
}
for (int i = 0; i < totalSensor; i++)
{
string id = models[comboBoxModel.SelectedIndex].selectedChannelsIndex[i];
double value = eegData[id][counter];
dataTesting[i] = value;
}
fft.Init(totalSensor);
double[] dataEegFreq = fft.DoWork(dataTesting);
List <double[]> result = dsp.Decomposes(dataEegFreq);
for (int i = 0; i < totalSensor; i++)
{
if (checkShowChart.Checked)
{
chartDeltaTheta.Series[i].Points.AddY(result[0][i]);
chartAlpha.Series[i].Points.AddY(result[1][i]);
chartBeta.Series[i].Points.AddY(result[2][i]);
chartGamma.Series[i].Points.AddY(result[3][i]);
if (counter > LIMIT_WINDOW_CHART)
{
chartDeltaTheta.Series[i].Points.RemoveAt(0);
chartAlpha.Series[i].Points.RemoveAt(0);
chartBeta.Series[i].Points.RemoveAt(0);
chartGamma.Series[i].Points.RemoveAt(0);
}
}
fitur.Add(result[0][i]);
fitur.Add(result[1][i]);
fitur.Add(result[2][i]);
fitur.Add(result[3][i]);
}
if (checkShowChart.Checked)
{
chartEeg.ChartAreas[0].RecalculateAxesScale();
chartDeltaTheta.ChartAreas[0].RecalculateAxesScale();
chartAlpha.ChartAreas[0].RecalculateAxesScale();
chartBeta.ChartAreas[0].RecalculateAxesScale();
chartGamma.ChartAreas[0].RecalculateAxesScale();
}
if (totalSensor > 1)
{
for (int i = 0; i < totalSensor; i++)
{
for (int j = i + 1; j < totalSensor; j++)
{
double val1 = result[0][i];
double val2 = result[0][j];
double powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = result[1][i];
val2 = result[1][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = result[2][i];
val2 = result[2][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
val1 = result[3][i];
val2 = result[3][j];
powerDiff = Math.Abs((val1 - val2) / (val1 + val2));
fitur.Add(powerDiff);
}
}
}
if ((buffer + 1) == bufferDecision)
{
int classifyResult = knn.Classify(fitur, 1);
if (compareClass == classifyResult)
{
totalCorrectOutput++;
}
labelMovement.Text = classesCommand[classifyResult];
if (counterOutput == 20)
{
double akurasi = (double)totalCorrectOutput / counterOutput * 100;
timer.Enabled = false;
MessageBox.Show("akurasi: " + akurasi + "%");
}
fitur = new List <double>();
buffer = -1;
tickSecond++;
counterOutput++;
labelTickSecond.Text = tickSecond.ToString();
}
++buffer;
++counter;
}
