public void AddVectorToMatrix(BrainWaveVector newWaveVector)
{
if (waveMatrix.Count() != 0)
{
if (EucledianDistance(waveMatrix[waveMatrix.Count() - 1].ReturnVector(), newWaveVector.ReturnVector()) != 0)
{
waveMatrix.Add(newWaveVector);
}
}
else
{
waveMatrix.Add(newWaveVector);
}
}
static void OnDataReceived(object sender, EventArgs e)
{
BrainWaveVector waveVector = new BrainWaveVector(trainingClassNumber);
//Device d = (Device)sender;
form.SetStatusText("Receiving Data");
Device.DataEventArgs de = (Device.DataEventArgs)e;
DataRow[] tempDataRowArray = de.DataRowArray;
TGParser tgParser = new TGParser();
tgParser.Read(de.DataRowArray);
// MentalEffort mentalEffort = new MentalEffort();
// mentalEffort.CalculateMentalEffort(, TGsensorType.);
/* Loops through the newly parsed data of the connected headset*/
// The comments below indicate and can be used to print out the different data outputs.
for (int i = 0; i < tgParser.ParsedData.Length; i++)
{
#region Build waveVector with ParsedData
if (tgParser.ParsedData[i].ContainsKey("Raw"))
{
if (collectClickTrainingSample)
{
rawTrainingSampleArray.Add(tgParser.ParsedData[i]["Raw"]);
}
else if (finishedTraining)
{
AddData(tgParser.ParsedData[i]["Raw"]);
}
}
#region Parse EEG Power Values
if (tgParser.ParsedData[i].ContainsKey("EegPowerTheta"))
{
waveVector.AddValue(0, tgParser.ParsedData[i]["EegPowerTheta"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerDelta"))
{
waveVector.AddValue(1, tgParser.ParsedData[i]["EegPowerDelta"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerAlpha1"))
{
waveVector.AddValue(2, tgParser.ParsedData[i]["EegPowerAlpha1"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerAlpha2"))
{
waveVector.AddValue(3, tgParser.ParsedData[i]["EegPowerAlpha2"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerBeta1"))
{
waveVector.AddValue(4, tgParser.ParsedData[i]["EegPowerBeta1"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerBeta2"))
{
waveVector.AddValue(5, tgParser.ParsedData[i]["EegPowerBeta2"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerGamma1"))
{
waveVector.AddValue(6, tgParser.ParsedData[i]["EegPowerGamma1"]);
}
if (tgParser.ParsedData[i].ContainsKey("EegPowerGamma2"))
{
waveVector.AddValue(7, tgParser.ParsedData[i]["EegPowerGamma2"]);
}
#endregion
#endregion
#region ParsedData if statements from sample - kept for reference
if (tgParser.ParsedData[i].ContainsKey("Raw"))
{
//Console.WriteLine("Raw Value:" + tgParser.ParsedData[i]["Raw"]);
// form.SetDataText((tgParser.ParsedData[i]["Raw"]) +"\t");
// AddData(tgParser.ParsedData[i]["Raw"]);
}
if (tgParser.ParsedData[i].ContainsKey("PoorSignal"))
{
//The following line prints the Time associated with the parsed data
//Console.WriteLine("Time:" + tgParser.ParsedData[i]["Time"]);
//A Poor Signal value of 0 indicates that your headset is fitting properly
// Console.WriteLine("Poor Signal:" + tgParser.ParsedData[i]["PoorSignal"]);
// poorSig = (byte)tgParser.ParsedData[i]["PoorSignal"];
}
if (tgParser.ParsedData[i].ContainsKey("Attention"))
{
//Console.WriteLine("Att Value:" + tgParser.ParsedData[i]["Attention"]);
}
if (tgParser.ParsedData[i].ContainsKey("Meditation"))
{
//Console.WriteLine("Med Value:" + tgParser.ParsedData[i]["Meditation"]);
}
if (tgParser.ParsedData[i].ContainsKey("BlinkStrength"))
{
//Console.WriteLine("Eyeblink " + tgParser.ParsedData[i]["BlinkStrength"]);
}
/* if (tgParser.ParsedData[i].ContainsKey("MentalEffort"))
* {
*
* form.SetDataText((tgParser.ParsedData[i]["MentalEffort"]) + "\t");
* AddData(tgParser.ParsedData[i]["MentalEffort"]);
* } */
/* foreach(string key in tgParser.ParsedData[i].Keys.ToArray())
* {
* form.SetDataText(key + "\t");
* } */
// CalculateAndDisplayAverage();
#endregion
if (finishedTraining)
{
if (waveVector.HasValue())
{
//Check if correspondes to ClickTrainingSample
// form.SetClickAverageText(CheckForEvent(clickTrainingSample, waveVector).ToString());
// form.SetDataText(waveVector.ReturnVector()[1].ToString() + '\t');
}
bool sampleMatch = CheckForEvent(dataBuffer, rawTrainingSampleAverage, out rawDataAverage);
bool complexSampleMatch = CheckForEvent(sampleMagnitudeAverage, dataComplex, out rawDataMagnitudeAverage);
form.SetRelaxAverageText(sampleMatch.ToString());
form.SetClickAverageText(rawTrainingSampleAverage.ToString());
//form.SetDataText(rawDataAverage.ToString() + '\t');
form.SetDataText(rawDataMagnitudeAverage.ToString() + '\t');
form.SetMagnitudeEventText(complexSampleMatch.ToString());
if (sampleMatch)
{
if (matchCounter >= numberOfConsecutiveMatchesRequired && allowKeyEvent)
{
// TieIntoWindow();
// allowKeyEvent = false;
matchCounter = 0;
}
matchCounter++;
}
else
{
matchCounter = 0;
}
if (complexSampleMatch)
{
if (transformedMatchCounter >= numberOfConsecutiveMatchesRequired && allowKeyEvent)
{
TieIntoWindow();
allowKeyEvent = false;
ResetDataBuffer();
//timer ensures event cannot be read for 1 second
// form.StartEventRestTimer();
transformedMatchCounter = 0;
}
transformedMatchCounter++;
}
else
{
transformedMatchCounter = 0;
allowKeyEvent = true;
}
}
else if (collectClickTrainingSample && waveVector.HasValue())
{
clickTrainingSample.AddVectorToMatrix(waveVector);
jointTrainingSample.AddVectorToMatrix(waveVector);
}
else if (collectRelaxTrainingSample && waveVector.HasValue())
{
relaxTrainingSample.AddVectorToMatrix(waveVector);
jointTrainingSample.AddVectorToMatrix(waveVector);
}
}
}
public static bool CheckForEvent(BrainWaveMatrix trainedSampleMatrix, BrainWaveVector collectedVector)
{
return(trainedSampleMatrix.CheckForMatch(collectedVector.ReturnVector(), out distance));
}