Condro2010 thesis slide_v3
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Condro2010 thesis slide_v3

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This is my master thesis presentation in 2010: "Improvement of Response Times in SSVEP-based Brain-Computer Interface", in the University of Bremen, Germany.

This is my master thesis presentation in 2010: "Improvement of Response Times in SSVEP-based Brain-Computer Interface", in the University of Bremen, Germany.

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Condro2010 thesis slide_v3 Condro2010 thesis slide_v3 Presentation Transcript

  • Master Thesis Improvement of Response Times in SSVEP-based BCI Ignatius Sapto Condro Atmawan Bisawarna Matrikel Number 2113914 Supervised by: Prof. Dr.-Ing. Axel Gräser Dr.-Ing. Ivan Volosyak Thorsten Lüth, Dipl.-Ing.
  • Content• Introduction• Simulation• Implementation• Experiment• ConclusionImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 2
  • SSVEP based BCI (I)Brain-Computer Interface (BCI) is a communication system in which messages or commands that an individual sends to the external world do not pass through the brain’s normal output pathways of peripheral nerves and muscles.(Wolpaw, et al. 2002. Clinical Neurophysiology)Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 3
  • SSVEP based BCI (II)Steady-state visual evoked potential• Electrophysiological response of the visual cortex• Resonance phenomena• Rapidly repeating visual stimulus: flickering LED or lamp, blinking picture on screen and other light sources.• Frequency above 4 HzImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 4
  • Bremen SSVEP-based BCI• Brain-Computer Interface (BCI) research at the IAT of the University of Bremen started in 2005• It is intended to make a faster BCI system, but the system should not lose its accuracy (too much)• IAT Bremen BCI uses minimum energy combination (MEC) algorithm to detect SSVEP• With MEC, the signal power of a certain frequency, as well as the SNR, are estimated.• The SNR is used for classification with thresholdingImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 5
  • Time Series Prediction forBremen BCIImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 6
  • Time Series Prediction • Three-point Quadratic Model • Regression • Logical Trend-based • Kalman FilterImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 7
  • Quadratic ModelImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 8
  • RegressionImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 9
  • Logical Trend-based DecisionThe present value should be larger than the previous value.• Three points• More pointsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 10
  • Kalman FilterKalman Filter is used with state space model of a systemIt contains 2 steps:• Prediction• Measurement or updatingWhat is updated?• State• CovarianceHow they are updated?• Simplified form• Särkää´s form• Joseph´s formImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 11
  • Simulation:Three-point Quadratic ModelImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 12
  • Simulation:Regression, 5 delay tapsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 13
  • Simulation:Regression, 8 delay tapsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 14
  • Simulation:Logical Trend-based Decision• Decision is based on trends or gradients (of the SNRs).• The result contains 6 commands, correlated to 5 LEDs and no selection.• There is redundancy, so the values (of the SNRs) have to be used.• If redundancy happens, the maximum value is selected.Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 15
  • Simulation:Logical Trend-based Decision,2 delay taps (three points)Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 16
  • Simulation:Logical Trend-based Decision,5 delay tapsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 17
  • Simulation:Logical Trend-based Decision,8 delay tapsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 18
  • Simulation:Kalman Filter, 5 delay taps,Simplified formImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 19
  • Simulation:Kalman Filter, 5 delay taps,Särkää´s formImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 20
  • Simulation:Kalman Filter, 5 delay taps,Joseph´s formImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 21
  • Software Implementation:Time Series Prediction in BCI2000Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 22
  • Experiment• 11 subjects (2 females, 9 males), with age range 21-30 years old.• 4 LEDs• 8 EEG electrodes• Sampling frequency = 2048 Hz• Segment Length = 2 sThere are 3 experiments• Experiment I : 8 subjects• Experiment II : 7 subjects• Experiment III: 3 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 23
  • Experiment:ProtocolImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 24
  • Experiment:Measured Parameters• Speed• Accuracy• Information transfer rate (ITR)Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 25
  • Experiment I : SpeedIdle Period 1 s, 8 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 26
  • Experiment I : AccuracyIdle Period 1 s, 8 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 27
  • Experiment I : ITRIdle Period 1 s, 8 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 28
  • Experiment II : SpeedIdle Period 2 s, 7 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 29
  • Experiment II : AccuracyIdle Period 2 s, 7 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 30
  • Experiment II : ITRIdle Period 2 s, 7 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 31
  • Experiment III : SpeedIdle Period 2 s, 3 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 32
  • Experiment III : AccuracyIdle Period 2 s, 3 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 33
  • Experiment III : ITRIdle Period 2 s, 3 subjectsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 34
  • Conclusion• Time Series Prediction can improve response time• Regression model, with 8 delay taps, has the best ITR• Kalman Filter can improve ITR, if 5 or 10 steps are chosen.• The optimal forms of Kalman Filter are the simplified and the Särkää´s• Joseph´s form of Kalman Filter has failed in simulation, so it is not implemented.• The Quadratic Three-point model increases the speed but lose the accuracy too much so it shows poor ITR• Logical trend-based decision has failed in simulation, so it is not implemented• A decision based on only trend or gradient does not work• Idle period should not be lower than segment lengthImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 35
  • Future works• The Time Series Prediction algorithms can be implemented in other BCI applications: spelling, moving wheelchair or robots and so on.• The transient response of Kalman Filter can be observed and recorded by adding more C++ code for data acquisition.Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 36
  • Thank YouImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 37
  • Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 38
  • Back UpImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 39
  • Kalman Filter: state space• System Model• Measurement Model• Output ModelImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 40
  • Kalman Filter:System model for TSP orImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 41
  • Kalman Filter:Measurement Model for TSPImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 42
  • Kalman Filter:Output Model for TSPm is number of steps ahead for predictionImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 43
  • Kalman Filter:Prediction stepImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 44
  • Kalman Filter:Measurement & Updating Step (I)Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 45
  • Kalman Filter:Measurement & Updating Step (II)Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 46
  • Kalman Filter:Measurement & Updating Step (III)Updated (a posteriori) covariance estimate• Simplified form•• Särkää´s form•• Joseph´s formImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 47
  • Kalman Filter:Measurement & Updating Step (III)Updated (a posteriori) covariance estimate• Simplified form• with• Särkää´s form• with• Joseph´s formImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 48
  • Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 49
  • Simulation• MATLAB R2006b (version 7.3) from Mathworks is used• The data used is from the experiment with the visor cap (wearable SSVEP stimulator).• 6 EEG electrodes• 5 LEDs with different frequencies• Sampling frequency = 128 Hz• Segment Length = 2 sImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 50
  • Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 51
  • Software Implementation• Two standard C++ classes: cRegression3 and cKalmanIATBCI.• BCI2000 - version 2 can be compiled only with Borland C++ Builder 6.0• ClassifierConnect.Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 52
  • Software Implementation:C++ classes• Method double getRegression(double dYInput,int Nt)• Method double getKalmanFilter(double dInput, double dVariance, int iStep, bool bChoice)Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 53
  • Software Implementation:Block Diagram• Pvi = Probability values at SNR channel i• Pvi can be called Normalised SNRsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 54
  • Software Implementation:BCI2000BCI2000 is a general-purpose system for BCIBCI2000 supports different kinds of• Signal acquisition devices• Signal processing• BCI applicationsImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 55
  • Software Implementation:BCI2000, filtering module IImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 56
  • Software Implementation:ClassifierConnectImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 57
  • Hardware Implementation• Porti7, with 32 channels, as USB Amplifier.• LED Array.• LED Controller, with PIC 16F877.Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 58
  • Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 59
  • Experiment:EEG Cap ConfigurationImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 60
  • Experiment:Subject InformationImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 61
  • Experiment:Subject ParticipationImprovement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 62
  • Improvement of Response Times in SSVEP-BCIIgnatius Sapto Condro Atmawan, 2010 63