void
SignalDisplay::AdaptTo( const GenericSignal& inSignal )
{
// Any changes in the signal size that we must react to?
bool reconfigure = false;
if( inSignal.Elements() > mNumSamples )
{
OSMutex::Lock lock( mDataLock );
SetNumSamples( inSignal.Elements() );
reconfigure = true;
}
if( inSignal.Channels() != mData.Channels() )
{
OSMutex::Lock lock( mDataLock );
int newNumDisplayGroups = ( inSignal.Channels() - mMarkerChannels - 1 ) / mChannelGroupSize + 1;
if( mNumDisplayGroups == 0 )
mNumDisplayGroups = min<int>( newNumDisplayGroups, cInitialMaxDisplayGroups );
else if( newNumDisplayGroups < mNumDisplayGroups )
mNumDisplayGroups = newNumDisplayGroups;
reconfigure = true;
}
if( reconfigure )
{
OSMutex::Lock lock( mDataLock );
mData = GenericSignal( inSignal.Channels(), mNumSamples );
SetDisplayGroups( DisplayGroups() );
SyncLabelWidth();
mSampleCursor = 0;
Invalidate();
}
}
// **************************************************************************
// Function: Process
// Purpose: This function is called within the data acquisition loop
// it fills its output signal with values
// and does not return until all data has been acquired.
// Parameters: References to input signal (ignored) and output signal.
// Returns: N/A
// **************************************************************************
void NeuroSkyADC::Process( const GenericSignal&, GenericSignal& Output )
{
// Clear out old data
mDataBlock.clear();
// Fill a Sample Block
for( int i = 0; i < Output.Elements(); i++ )
{
// Read Packets until new raw data comes in
while( true )
{
TG_ReadPackets( mConnectionID, 1 );
if( TG_GetValueStatus( mConnectionID, TG_DATA_RAW ) != 0 )
break;
Sleep( 1 );
}
// Push the newest data into the data block
mDataBlock.push_back( TG_GetValue( mConnectionID, TG_DATA_RAW ) );
}
// Fill the output with that data
for( int sample = 0; sample < Output.Elements(); ++sample )
Output( 0, sample ) = mDataBlock[ sample ];
}
void
Normalizer::Process( const GenericSignal& Input, GenericSignal& Output )
{
if( mDoAdapt )
{
for( size_t channel = 0; channel < mBufferConditions.size(); ++channel )
for( size_t buffer = 0; buffer < mBufferConditions[ channel ].size(); ++buffer )
{
double label = mBufferConditions[ channel ][ buffer ].Evaluate( &Input );
if( label != 0 )
for( int sample = 0; sample < Input.Elements(); ++sample )
mDataBuffers[ channel ][ buffer ].Put( Input( channel, sample ) );
}
if( mpUpdateTrigger != NULL )
{
bool currentTrigger = mpUpdateTrigger->Evaluate( &Input );
if( currentTrigger && !mPreviousTrigger )
Update();
mPreviousTrigger = currentTrigger;
}
else
Update();
}
for( int channel = 0; channel < Input.Channels(); ++channel )
for( int sample = 0; sample < Input.Elements(); ++sample )
Output( channel, sample )
= ( Input( channel, sample ) - mOffsets[ channel ] ) * mGains[ channel ];
}
void
FFTFilter::Process( const GenericSignal& Input, GenericSignal& Output )
{
if( mFFTOutputSignal == eInput )
Output = Input;
for( size_t i = 0; i < mFFTInputChannels.size(); ++i )
{
// Copy input signal values to the value buffer.
vector<float>& buffer = mValueBuffers[ i ];
int inputSize = Input.Elements(),
bufferSize = static_cast<int>( buffer.size() );
// Move old values towards the beginning of the buffer, if any.
for( int j = 0; j < bufferSize - inputSize; ++j )
buffer[ j ] = buffer[ j + inputSize ];
// Copy new values to the end of the buffer;
// buffer size may be greater or less than input size.
for( int j = ::max( 0, bufferSize - inputSize ); j < bufferSize; ++j )
buffer[ j ] = static_cast<float>( Input( mFFTInputChannels[ i ], j + inputSize - bufferSize ) );
// Prepare the buffer.
if( mFFTWindow == eNone )
for( int j = 0; j < bufferSize; ++j )
mFFT.Input( j ) = buffer[ j ];
else
for( int j = 0; j < bufferSize; ++j )
mFFT.Input( j ) = buffer[ j ] * mWindow[ j ];
// Compute the power spectrum and visualize it if requested.
mFFT.Compute();
if( mVisualizeFFT || mFFTOutputSignal == ePower )
{
int maxIdx = mPowerSpectrum.Channels() - 1;
double normFactor = 1.0 / bufferSize;
mPowerSpectrum( maxIdx, 0 ) = mFFT.Output( 0 ) * mFFT.Output( 0 ) * normFactor;
for( int k = 1; k < ( bufferSize + 1 ) / 2; ++k )
mPowerSpectrum( maxIdx - k, 0 ) = (
mFFT.Output( k ) * mFFT.Output( k ) +
mFFT.Output( bufferSize - k ) * mFFT.Output( bufferSize - k ) ) * normFactor;
if( bufferSize % 2 == 0 )
mPowerSpectrum( maxIdx - bufferSize / 2, 0 )
= mFFT.Output( bufferSize / 2 ) * mFFT.Output( bufferSize / 2 ) * normFactor;
}
if( mVisualizeFFT )
mVisualizations[ i ].Send( mPowerSpectrum );
if( mFFTOutputSignal == ePower )
{
for( int j = 0; j < Output.Elements(); ++j )
Output( i, j ) = mPowerSpectrum( mPowerSpectrum.Channels() - 1 - j, 0 );
}
else if( mFFTOutputSignal == eHalfcomplex )
{
double normFactor = 1.0 / ::sqrt( 1.0 * bufferSize );
for( int j = 0; j < Output.Elements(); ++j )
Output( i, j ) = mFFT.Output( j ) * normFactor;
}
}
}
////////////////////////////////////////////////////////////////////////////////
// MatlabEngine::DoubleMatrix definitions //
////////////////////////////////////////////////////////////////////////////////
MatlabEngine::DoubleMatrix::DoubleMatrix( const GenericSignal& inSignal )
: vector<vector<double> >( inSignal.Channels(), vector<double>( inSignal.Elements() ) )
{
for( size_t channel = 0; channel < size(); ++channel )
for( size_t sample = 0; sample < ( *this )[ channel ].size(); ++sample )
( *this )[ channel ][ sample ] = inSignal( channel, sample );
}
void
CustomFIRFilter::Process( const GenericSignal& Input, GenericSignal& Output )
{
if( mBuffer.empty() || mFilter.size() == 0 )
Output = Input;
else for( size_t channel = 0; channel < mBuffer.size(); ++channel )
{
int bufferLength = static_cast<int>( mBuffer[channel].size() ),
filterLength = static_cast<int>( mFilter.size() ),
inputLength = Input.Elements();
// Move buffer content towards the buffer's begin.
for( int sample = 0; sample < bufferLength - inputLength; ++sample )
mBuffer[channel][sample] = mBuffer[channel][sample + inputLength];
// Copy current input into the buffer's end.
for( int sample = 0; sample < inputLength; ++sample )
mBuffer[channel][bufferLength - inputLength + sample] = Input( channel, sample );
// Compute buffer's convolution with coefficient vector.
DataVector result( 0.0, inputLength );
for( int sample = 0; sample < inputLength; ++sample )
result[sample] = inner_product( &mFilter[0], &mFilter[filterLength], &mBuffer[channel][sample], 0.0 );
// Compute output.
for( int sample = 0; sample < inputLength; ++sample )
Output( channel, sample ) = result[sample];
}
}
// The Process() function is called from the main thread in regular intervals.
void
BufferedADC::Process( const GenericSignal&,
GenericSignal& Output )
{
this->OnProcess();
AcquisitionBuffer& buffer = mpBuffers[mReadCursor];
++mReadCursor %= mSourceBufferSize;
Lock lock( buffer );
if( !IsAcquiring() )
{
bcierr_ << ( mError.empty() ? "Acquisition Error" : mError );
if( State( "Running" ) )
State( "Running" ) = 0;
return;
}
if( buffer.Signal.Channels() == Output.Channels() )
Output = buffer.Signal;
else
{
const LabelIndex& labels = mAcquisitionProperties.ChannelLabels();
for( int el = 0; el < Output.Elements(); ++el )
{
for( int ch = 0; ch < Output.Channels(); ++ch )
Output( ch, el ) = buffer.Signal( ch, el );
for( int ch = Output.Channels(); ch < buffer.Signal.Channels(); ++ch )
State( labels[ch].c_str() + 1 )( el ) = static_cast<State::ValueType>( buffer.Signal( ch, el ) );
}
}
State( "SourceTime" ) = buffer.TimeStamp;
}
void
BCI2000OutputFormat::Write( ostream& os, const GenericSignal& inSignal, const StateVector& inStatevector )
{
switch( mInputProperties.Type() )
{
case SignalType::int16:
case SignalType::float32:
case SignalType::int32:
// Note that the order of Elements and Channels differs from the one in the
// socket protocol.
for( int j = 0; j < inSignal.Elements(); ++j )
{
for( int i = 0; i < inSignal.Channels(); ++i )
inSignal.WriteValueBinary( os, i, j );
os.write(
reinterpret_cast<const char*>( inStatevector( min( j, inStatevector.Samples() - 1 ) ).Data() ),
inStatevector.Length()
);
}
break;
default:
bcierr << "Unsupported signal data type" << endl;
}
}
void
LinearClassifier::Process( const GenericSignal& Input, GenericSignal& Output )
{
for( int ch = 0; ch < Output.Channels(); ++ch )
for( int el = 0; el < Output.Elements(); ++el )
Output( ch, el ) = 0.0;
for( size_t i = 0; i < mWeights.size(); ++i )
Output( mOutputChannels[ i ], 0 )
+= Input( mInputChannels[ i ], mInputElements[ i ] ) * mWeights[ i ];
}
// **************************************************************************
// Function: ADReadDataBlock
// Purpose: This function is called within fMain->MainDataAcqLoop()
// it fills the already initialized array RawEEG with values
// and DOES NOT RETURN, UNTIL ALL DATA IS ACQUIRED
// Parameters: N/A
// Returns: 0 ... on error
// 1 ... no error
// **************************************************************************
void ModularEEGADC::Process( const GenericSignal&, GenericSignal& signal )
{
int value;
const long maxvalue = ( 1L << 15 ) - 1,
minvalue = - ( 1L << 15 );
// generate the sine wave and write it into the signal
for (int sample=0; sample<signal.Elements(); sample++)
{
read_channels(devicehandle,protocol);
for (int channel=0; channel<signal.Channels(); channel++)
{
value = PACKET.buffer[channel];
if( value > maxvalue ) value = maxvalue;
if( value < minvalue ) value = minvalue;
signal(channel, sample) = (short)value;
}
}
mCount=mCount+signal.Elements();
}
void
EDFFileWriterBase::PutBlock( const GenericSignal& inSignal, const StateVector& inStatevector )
{
for( int i = 0; i < inSignal.Channels(); ++i )
for( int j = 0; j < inSignal.Elements(); ++j )
GDF::Num<T>( inSignal( i, j ) ).WriteToStream( OutputStream() );
for( size_t i = 0; i < mStateNames.size(); ++i )
GDF::PutField< GDF::Num<GDF::int16> >(
OutputStream(),
inStatevector.StateValue( mStateNames[ i ] )
);
}
// **************************************************************************
// Function: Process
// Purpose: This function is called within the data acquisition loop
// it fills its output signal with values
// and does not return until all data has been acquired.
// Parameters: References to input signal (ignored) and output signal.
// Returns: N/A
// **************************************************************************
void NicoletOneADC::Process( const GenericSignal&, GenericSignal& Output )
{
// Grab the data for each channel and sample, push it into the output
bool newData = false;
while( !newData )
{
// Grab new information from thread
bool newTsInfo = false;
newData = !( mNT->ExtractData( Output.Channels(), Output.Elements(), mDataBlock, newTsInfo ) );
// If we have a new data block, we can fill out sample block
if( newData )
for( int sample = 0; sample < Output.Elements(); ++sample )
for( int channel = 0; channel < Output.Channels(); ++channel )
Output( channel, sample ) = mDataBlock[channel][sample];
// If we have new TsInfo we need to see if its still valid.
if( newTsInfo )
{
// We need to check the signal properties to verify that all is well.
// Check the number of channels
int numChannels = 0;
while( mNT->GetNumChannels( &numChannels ) )
Sleep( 10 );
if( mChannels != numChannels )
bcierr << "The number of channels reported has changed. Experiment is ending. Device is reporting " << numChannels << " channels" << endl;
// Check Sampling Rate
double rate = 0.0f;
while( mNT->GetSampleRate( &rate ) )
Sleep( 10 );
if( mSamplingRate != (int)rate )
bcierr << "Sampling Rate has changed. Experiment is ending. Device is reporting " << (int)rate << " Hz." << endl;
}
// Needed?
Sleep( 10 );
}
}
void
SpatialFilter::Process( const GenericSignal& Input, GenericSignal& Output )
{
// Actually perform Spatial Filtering on the input and write it into the output signal.
for( int sample = 0; sample < Input.Elements(); ++sample )
for( int outChannel = 0; outChannel < Output.Channels(); ++outChannel )
{
double value = 0;
for( int inChannel = 0; inChannel < Input.Channels(); ++inChannel )
value += mFilterMatrix[ outChannel ][ inChannel ] * Input( inChannel, sample );
Output( outChannel, sample ) = value;
}
}
void
RDAClientADC::DoAcquire( GenericSignal& Output )
{
if( !mConnection.ReceiveData( Output ) )
Error( "Lost connection to VisionRecorder software" );
else if( mAddMarkerChannel )
{
int from = Output.Channels() - 2,
to = from + 1;
for( int el = 0; el < Output.Elements(); ++el )
Output( to, el ) = Output( from, el );
}
}
void
BrainVisionGDRConverter::OutputSignal( const GenericSignal& inSignal, long /*inSamplePos*/ )
{
Idle();
for( int sample = 0; sample < inSignal.Elements(); ++sample )
for( int channel = 0; channel < inSignal.Channels(); ++channel )
{
float value = inSignal( channel, sample );
mDataFile.write( reinterpret_cast<const char*>( &value ), sizeof( value ) );
}
if( !mDataFile )
bcierr << "Error writing data file" << endl;
}
void
AlignmentFilter::Process( const GenericSignal& Input, GenericSignal& Output )
{
if( mAlign ) // Perform the alignment on the input and write it into the output signal.
for( int channel = 0; channel < Input.Channels(); ++channel )
for( int sample = 0; sample < Input.Elements(); ++sample )
{
Output( channel, sample ) =
Input( channel, sample ) * mWeightCur[ channel ]
+ mWeightPrev[ channel ] * mPrevSample[ channel ];
mPrevSample[ channel ] = Input( channel, sample );
}
else // No alignment.
Output = Input;
}
void
RDAClientADC::Process( const GenericSignal&, GenericSignal& Output )
{
for( int sample = 0; sample < Output.Elements(); ++sample )
for( int channel = 0; channel < Output.Channels(); ++channel )
{
if( !mInputQueue )
{
bcierr << "Lost connection to VisionRecorder software" << endl;
return;
}
Output( channel, sample ) = mInputQueue.front();
mInputQueue.pop();
}
}
void
BAlertADC::Process( const GenericSignal& Input, GenericSignal& Output )
{
float fBuffer[768];
if(BAlertWaitForData(fBuffer,mSampleBlockSize))
{
int i=0;
for( int el = 0; el < Output.Elements(); el++ )
for( int ch = 0; ch < Output.Channels(); ch++ )
Output( ch, el ) = fBuffer[ i++ ];
}
else
{
bcierr<< "ABM: Data acquisition failed..." << endl;
}
}
void
ASCIIConverter::OutputSignal( const GenericSignal& inSignal, long long /*inSamplePos*/ )
{
Idle();
for( int sample = 0; sample < inSignal.Elements(); ++sample )
{
for( int channel = 0; channel < inSignal.Channels(); ++channel )
{
double value = inSignal( channel, sample );
mDataFile << ' ' << value;
}
for( size_t state = 0; state < mStateValues.size(); ++state )
mDataFile << ' ' << mStateValues[ state ];
mDataFile << '\n';
}
if( !mDataFile )
bcierr << "Error writing data file" << endl;
}
void
ConnectorInput::Process( const GenericSignal& Input, GenericSignal& Output )
{
try
{
Output = Input;
string buffer;
while( mConnection && mConnection.rdbuf()->in_avail() > 0 )
buffer += mConnection.get();
istringstream iss( buffer );
string name;
while( iss >> name )
{
double value;
string remainder;
if( !std::getline( iss >> value, remainder ) || !remainder.empty() )
throw bciexception( "Malformed input, expected state name, followed by a single number as a value,"
"space-separated, and terminated with a newline character" );
bool match = false;
for( vector<string>::const_iterator i = mInputFilters.begin(); i != mInputFilters.end() && !match; ++i )
match = match || WildcardMatch( *i, name, false );
if( match )
{
if( name.find( "Signal(" ) == 0 )
{
istringstream iss( name.substr( name.find( '(' ) ) );
char ignore;
int channel = 0,
element = 0;
if( !( iss >> ignore >> channel >> ignore >> element >> ignore ) )
throw bciexception( "Incorrect Signal index syntax: " << name );
if( channel >= Input.Channels() || element >= Input.Elements() )
throw bciexception( "Received signal index out-of-bounds: " << name );
Output( channel, element ) = value;
}
else
{
if( !States->Exists( name ) )
throw bciexception( "Ignoring value for non-existent " << name << " state" );
State( name.c_str() ) = static_cast<int>( value );
}
}
}
void
ComplexDemodulator::Process( const GenericSignal& Input, GenericSignal& Output )
{
for( int channel = 0; channel < Input.Channels(); ++channel )
{
for( int sample = 0; sample < Input.Elements(); ++sample )
{
mSignalBuffer[ channel ].push_back( Input( channel, sample ) );
mSignalBuffer[ channel ].erase( mSignalBuffer[ channel ].begin() );
}
for( size_t band = 0; band < mCoefficients.size(); ++band )
{
Output( channel, band )
= norm( inner_product(
mSignalBuffer[ channel ].begin(),
mSignalBuffer[ channel ].end(),
mCoefficients[ band ].begin(),
complex<double>( 0.0, 0.0 )
) );
}
}
}
void
AverageDisplay::Process( const GenericSignal& Input, GenericSignal& Output )
{
size_t targetCode = State( "TargetCode" );
if( targetCode == 0 && targetCode != mLastTargetCode )
{
size_t targetIndex = find( mTargetCodes.begin(), mTargetCodes.end(), mLastTargetCode ) - mTargetCodes.begin();
if( targetIndex == mTargetCodes.size() )
mTargetCodes.push_back( mLastTargetCode );
// End of the current target code run.
for( size_t i = 0; i < mChannelIndices.size(); ++i )
{
for( int power = 0; power <= maxPower; ++power )
{
// - If the target code occurred for the first time, adapt the power sums.
if( mPowerSums[ power ][ i ].size() <= targetIndex )
mPowerSums[ power ][ i ].resize( targetIndex + 1 );
// - Update power sum sizes.
if( mPowerSums[ power ][ i ][ targetIndex ].size() < mSignalOfCurrentRun[ i ].size() )
mPowerSums[ power ][ i ][ targetIndex ].resize( mSignalOfCurrentRun[ i ].size(), 0 );
}
#ifdef SET_BASELINE
if( mBaselineSamples[ i ] > 0 )
mBaselines[ i ] /= mBaselineSamples[ i ];
#endif // SET_BASELINE
// - Compute the power sum entries.
for( size_t j = 0; j < mSignalOfCurrentRun[ i ].size(); ++j )
{
#ifdef SET_BASELINE
mSignalOfCurrentRun[ i ][ j ] -= mBaselines[ i ];
#endif // SET_BASELINE
float summand = 1.0;
for( size_t power = 0; power < maxPower; ++power )
{
mPowerSums[ power ][ i ][ targetIndex ][ j ] += summand;
summand *= mSignalOfCurrentRun[ i ][ j ];
}
mPowerSums[ maxPower ][ i ][ targetIndex ][ j ] += summand;
}
}
// - Clear target run buffer.
for( size_t i = 0; i < mSignalOfCurrentRun.size(); ++i )
mSignalOfCurrentRun[ i ].clear();
#ifdef SET_BASELINE
for( size_t i = 0; i < mBaselines.size(); ++i )
{
mBaselineSamples[ i ] = 0;
mBaselines[ i ] = 0;
}
#endif // SET_BASELINE
// - Compute and display the averages.
for( size_t channel = 0; channel < mVisualizations.size(); ++channel )
{
size_t numTargets = mPowerSums[ maxPower ][ channel ].size(),
numSamples = numeric_limits<size_t>::max();
for( size_t target = 0; target < numTargets; ++target )
if( mPowerSums[ maxPower ][ channel ][ target ].size() < numSamples )
numSamples = mPowerSums[ maxPower ][ channel ][ target ].size();
// To minimize user confusion, always send target averages in ascending order
// of target codes. This ensures that colors in the display don't depend
// on the order of target codes in the task sequence once all target codes
// occurred.
// We cannot, however, avoid color changes when yet unknown target codes
// occur.
//
// The map is automatically sorted by its "key", so all we need to do
// is to put the target codes and their indices into it, using the
// target code as "key" and the index as "value", and later iterate over
// the map to get the indices sorted by their associated target code.
map<int, int> targetCodesToIndex;
for( size_t target = 0; target < numTargets; ++target )
targetCodesToIndex[ mTargetCodes[ target ] ] = target;
GenericSignal average( numTargets, numSamples );
LabelList labels;
for( map<int, int>::const_iterator target = targetCodesToIndex.begin();
target != targetCodesToIndex.end(); ++target )
{
for( size_t sample = 0; sample < numSamples; ++sample )
// If everything behaves as we believe it will,
// a division by zero is impossible.
// If it occurs nevertheless, the logic is messed up.
average( target->second, sample ) =
mPowerSums[ 1 ][ channel ][ target->second ][ sample ] /
mPowerSums[ 0 ][ channel ][ target->second ][ sample ];
ostringstream oss;
oss << "Target " << target->first;
string targetName = OptionalParameter( "TargetNames", target->first );
if( targetName != "" )
oss << " (" << targetName << ")";
labels.push_back( Label( target->second, oss.str() ) );
}
mVisualizations[ channel ].Send( CfgID::ChannelLabels, labels );
ostringstream oss;
oss << ( Parameter( "SampleBlockSize" ) / Input.Elements() / Parameter( "SamplingRate" ) ) << "s";
mVisualizations[ channel ].Send( CfgID::SampleUnit, oss.str() );
mVisualizations[ channel ].Send( average );
}
}
if( targetCode != 0 )
{
// Store the current signal to the end of the run buffer.
for( size_t i = 0; i < mChannelIndices.size(); ++i )
{
size_t signalCursorPos = mSignalOfCurrentRun[ i ].size();
mSignalOfCurrentRun[ i ].resize( signalCursorPos + Input.Elements() );
for( int j = 0; j < Input.Elements(); ++j )
mSignalOfCurrentRun[ i ][ signalCursorPos + j ] = Input( mChannelIndices[ i ], j );
}
#ifdef SET_BASELINE
if( OptionalState( "BaselineInterval" ) || OptionalState( "Baseline" ) )
{
for( size_t i = 0; i < mChannelIndices.size(); ++i )
{
mBaselineSamples[ i ] += Input.Elements();
for( int j = 0; j < Input.Elements(); ++j )
mBaselines[ i ] += Input( mChannelIndices[ i ], j );
}
}
#endif // SET_BASELINE
}
mLastTargetCode = targetCode;
Output = Input;
}
void
ARFilter::Process( const GenericSignal& Input, GenericSignal& Output )
{
for( int channel = 0; channel < Input.Channels(); ++channel )
{
DataVector& buf = mBuffer[ channel ];
// Shift buffer contents left:
size_t i = 0, j = Input.Elements();
while( j < buf.size() )
buf[ i++ ] = buf[ j++ ];
// Fill the rightmost part of the buffer with new input:
j = Input.Elements() - ( buf.size() - i );
while( i < buf.size() )
buf[ i++ ] = Input( channel, j++ );
const DataVector* inputData = &buf;
switch( mDetrend )
{
case none:
break;
case mean:
inputData = &Detrend::MeanDetrend( buf );
break;
case linear:
inputData = &Detrend::LinearDetrend( buf );
break;
default:
bcierr << "Unknown detrend option" << endl;
}
const Ratpoly<Complex>&
transferFunction = mMEMPredictor.TransferFunction( *inputData );
switch( mOutputType )
{
case SpectralAmplitude:
{
const std::valarray<float>& spectrum = mTransferSpectrum.Evaluate( transferFunction );
for( size_t bin = 0; bin < spectrum.size(); ++bin )
Output( channel, bin ) = sqrt( spectrum[ bin ] );
} break;
case SpectralPower:
{
const std::valarray<float>& spectrum = mTransferSpectrum.Evaluate( transferFunction );
for( size_t bin = 0; bin < spectrum.size(); ++bin )
Output( channel, bin ) = spectrum[ bin ];
} break;
case ARCoefficients:
{
const Polynomial<Complex>::Vector& coeff = transferFunction.Denominator().Coefficients();
for( size_t i = 1; i < coeff.size(); ++i )
Output( channel, i - 1 ) = coeff[ i ].real();
} break;
default:
bcierr << "Unknown output type" << endl;
}
}
}
void
P3TemporalFilter::Process( const GenericSignal& Input, GenericSignal& Output )
{
Output = GenericSignal( mOutputProperties );
if( mEpochsToAverage > 0 )
{
int curStimulusCode = State( "StimulusCode" );
mStimulusTypes[ curStimulusCode ] = State( "StimulusType" );
// If the StimulusBegin state is available, use it to detect stimulus onset.
// Otherwise, check whether StimulusCode has just switched to nonzero.
bool stimulusOnset = curStimulusCode > 0 &&
( OptionalState( "StimulusBegin", 0 ) || mPreviousStimulusCode == 0 );
if( stimulusOnset )
{ // First block of stimulus presentation -- create a new epoch buffer.
bcidbg( 3 ) << "New epoch for stimulus code #" << curStimulusCode << endl;
mEpochs[ curStimulusCode ].insert( new EpochBuffer( mOutputProperties ) );
}
mPreviousStimulusCode = curStimulusCode;
State( "StimulusCodeRes" ) = 0;
State( "StimulusTypeRes" ) = 0;
for( EpochMap::iterator i = mEpochs.begin(); i != mEpochs.end(); ++i )
{
int stimulusCode = i->first;
EpochSet obsoleteEpochs;
for( EpochSet::iterator j = i->second.begin(); j != i->second.end(); ++j )
{
( *j )->Process( Input );
if( ( *j )->EpochDone() )
{ // Move buffer data into the epoch sum associated with the stimulus code,
// and note the epoch buffer for later disposal.
bcidbg( 3 ) << "Epoch done for stimulus code #" << stimulusCode << endl;
if( mEpochSums[ stimulusCode ] == NULL )
{
bcidbg( 2 ) << "Allocating result buffer for stimulus code #" << stimulusCode << endl;
mEpochSums[ stimulusCode ] = new DataSum( mOutputProperties );
}
mEpochSums[ stimulusCode ]->Add( ( *j )->Data() );
obsoleteEpochs.insert( *j );
if( mEpochSums[ stimulusCode ]->Count() == mEpochsToAverage )
{ // When the number of required epochs is reached, copy the buffer average
// into the output signal, and set states appropriately.
bcidbg( 2 ) << "Reporting average for stimulus code #" << i->first
<< endl;
for( int channel = 0; channel < Output.Channels(); ++channel )
for( int sample = 0; sample < Output.Elements(); ++sample )
Output( channel, sample ) = ( *mEpochSums[ stimulusCode ] )( channel, sample ) / mEpochsToAverage;
State( "StimulusCodeRes" ) = stimulusCode;
State( "StimulusTypeRes" ) = mStimulusTypes[ stimulusCode ];
*mEpochSums[ stimulusCode ] = DataSum( mOutputProperties );
if( mVisualize && i->first - 1 < mVisSignal.Channels() )
{
for( int sample = 0; sample < Output.Elements(); ++sample )
mVisSignal( stimulusCode - 1, sample ) = Output( mTargetERPChannel, sample );
mVis.Send( mVisSignal );
}
}
}
}
for( EpochSet::iterator j = obsoleteEpochs.begin(); j != obsoleteEpochs.end(); ++j )
i->second.erase( *j );
// Epochs will be deallocated from the obsoleteEpochs class destructor.
}
}
}
void
FFTFilter::Process( const GenericSignal& inputSignal, GenericSignal& outputSignal )
{
for( size_t i = 0; i < mFFTInputChannels.size(); ++i )
{
// Copy the input signal values to the value buffer.
vector<float>& buffer = mValueBuffers[ i ];
int inputSize = inputSignal.Elements(),
bufferSize = buffer.size();
// Move old values towards the beginning of the buffer, if any.
for( int j = 0; j < bufferSize - inputSize; ++j )
buffer[ j ] = buffer[ j + inputSize ];
// Copy new values to the end of the buffer;
// the buffer size may be greater of smaller than the input size.
for( int j = ::max( 0, bufferSize - inputSize ); j < bufferSize; ++j )
buffer[ j ] = inputSignal( mFFTInputChannels[ i ], j + inputSize - bufferSize );
// Prepare the buffer.
if( mFFTWindow == eNone )
for( int j = 0; j < bufferSize; ++j )
mFFT.Input( j ) = buffer[ j ];
else
for( int j = 0; j < bufferSize; ++j )
mFFT.Input( j ) = buffer[ j ] * mWindow[ j ];
// Compute the power spectrum and visualize it if requested.
mFFT.Transform();
GenericSignal& spectrum = mSpectra[ i ];
if( mVisualizeFFT || mFFTOutputSignal == ePower )
{
float normFactor = 1.0 / bufferSize;
spectrum( 0, 0 ) = mFFT.Output( 0 ) * mFFT.Output( 0 ) * normFactor;
for( int k = 1; k < ( bufferSize + 1 ) / 2; ++k )
spectrum( k, 0 ) = (
mFFT.Output( k ) * mFFT.Output( k ) +
mFFT.Output( bufferSize - k ) * mFFT.Output( bufferSize - k ) ) * normFactor;
if( bufferSize % 2 == 0 )
spectrum( bufferSize / 2, 0 ) = mFFT.Output( bufferSize / 2 ) * mFFT.Output( bufferSize / 2 ) * normFactor;
}
else if( mFFTOutputSignal == eHalfcomplex )
{
float normFactor = 1.0 / ::sqrt( 1.0 * bufferSize );
for( int k = 0; k < bufferSize; ++k )
spectrum( k, 0 ) = mFFT.Output( k ) * normFactor;
}
if( mVisualizeFFT )
mVisualizations[ i ].Send( spectrum );
}
switch( mFFTOutputSignal )
{
case eInput:
outputSignal = inputSignal;
break;
case ePower:
case eHalfcomplex:
for( size_t channel = 0; channel < mSpectra.size(); ++channel )
for( int element = 0; element < mSpectra[ channel ].Channels(); ++element )
outputSignal( channel, element ) = mSpectra[ channel ]( element, 0 );
break;
default:
assert( false );
}
}
const GenericSignal&
BCI2000Viewer::ConstructDisplaySignal( long inPos, long inLength )
{
static GenericSignal result;
if( mFile.IsOpen() )
{
QApplication::setOverrideCursor( Qt::WaitCursor );
int i = 1;
vector<StateRef> states;
for( ; i < ui->channelList->count() && ( ui->channelList->item( i )->flags() & Qt::ItemIsUserCheckable ); ++i )
if( ui->channelList->item( i )->checkState() == Qt::Checked )
states.push_back( mFile.State( ui->channelList->item( i )->text().toLocal8Bit().constData() ) );
vector<int> channels;
int base = ++i;
for( ; i < ui->channelList->count() && ( ui->channelList->item( i )->flags() & Qt::ItemIsUserCheckable ); ++i )
if( ui->channelList->item( i )->checkState() == Qt::Checked )
channels.push_back( i - base );
GenericSignal signal( channels.size() + states.size(), inLength ),
statevalues( states.size(), inLength );
long sampleInFile = inPos;
for( long sample = 0;
sample < signal.Elements() && sampleInFile < mFile.NumSamples();
++sample, ++sampleInFile )
{
for( int channelIdx = 0; channelIdx < static_cast<int>( channels.size() ); ++channelIdx )
signal( channelIdx, sample ) = mFile.CalibratedValue( channels[ channelIdx ], sampleInFile );
mFile.ReadStateVector( sampleInFile );
for( size_t i = 0; i < states.size(); ++i )
statevalues( i, sample ) = states[i];
}
if( FilterActive() )
{
mFilter.Reset();
result = GenericSignal( signal.Properties() );
// run the filter twice to avoid transient artifacts
mFilter.Process( signal, result );
mFilter.Process( signal, result );
}
else
{
result = signal;
}
if( mRemoveMean )
{
for( size_t ch = 0; ch < channels.size(); ++ch )
{
double mean = 0.0;
for( int sample = 0; sample < result.Elements(); ++sample )
mean += result( ch, sample );
mean /= result.Elements();
for( int sample = 0; sample < result.Elements(); ++sample )
result( ch, sample ) -= mean;
}
}
for( int ch = 0; ch < statevalues.Channels(); ++ch )
for( int sample = 0; sample < statevalues.Elements(); ++sample )
result( ch + channels.size(), sample ) = statevalues( ch, sample );
QApplication::restoreOverrideCursor();
}
else
{
result = GenericSignal( 0, 0 );
}
return result;
}