Condro2010 thesis slide_v3

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
• Conclusion

Improvement of Response Times in SSVEP-BCI
Ignatius 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)


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 Hz


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
thresholding


Time Series Prediction for
Bremen BCI


Time Series Prediction

• Three-point Quadratic Model
• Regression
• Logical Trend-based
• Kalman Filter


Quadratic Model


Regression


Logical Trend-based Decision

The present value should be larger than
the previous value.

• Three points

• More points


Kalman Filter
Kalman Filter is used with state space model of a system

It contains 2 steps:
• Prediction
• Measurement or updating

What is updated?
• State
• Covariance

How they are updated?
• Simplified form
• Särkää´s form
• Joseph´s form


Simulation:
Three-point Quadratic Model


Simulation:
Regression, 5 delay taps


Simulation:
Regression, 8 delay taps


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.

Simulation:
Logical Trend-based Decision,
2 delay taps (three points)


Simulation:
5 delay taps


Simulation:
8 delay taps


Simulation:
Kalman Filter, 5 delay taps,
Simplified form


Simulation:
Särkää´s form


Simulation:
Joseph´s form


Software Implementation:
Time Series Prediction in BCI2000


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 s

There are 3 experiments
• Experiment I : 8 subjects
• Experiment II : 7 subjects
• Experiment III: 3 subjects

Experiment:
Protocol


Experiment:
Measured Parameters
• Speed

• Accuracy

• Information transfer rate (ITR)


Experiment I : Speed
Idle Period 1 s, 8 subjects


Experiment I : Accuracy


Experiment I : ITR


Experiment II : Speed


Experiment II : Accuracy


Experiment II : ITR


Experiment III : Speed


Experiment III : Accuracy


Experiment III : ITR


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 length

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.


Thank You


Back Up


Kalman Filter: state space
• System Model

• Measurement Model

• Output Model


Kalman Filter:
System model for TSP

or


Kalman Filter:
Measurement Model for TSP


Kalman Filter:
Output Model for TSP

m is number of steps ahead for prediction


Kalman Filter:
Prediction step


Kalman Filter:
Measurement & Updating Step (I)


Kalman Filter:
Measurement & Updating Step (II)


Kalman Filter:
Measurement & Updating Step (III)
Updated (a posteriori) covariance estimate
• Simplified form
•

•

• Joseph´s form


Kalman Filter:
Measurement & Updating Step (III)
Updated (a posteriori) covariance estimate
• Simplified form
• with

• with

• Joseph´s form


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 s


Software Implementation

• Two standard C++ classes:
cRegression3 and cKalmanIATBCI.
• BCI2000 - version 2 can be compiled
only with Borland C++ Builder 6.0
• ClassifierConnect.


C++ classes
• Method double getRegression(double dYInput,int Nt)

• Method double getKalmanFilter(double dInput, double dVariance,
int iStep, bool bChoice)


Block Diagram

• Pvi = Probability values at SNR channel i
• Pvi can be called Normalised SNRs


BCI2000
BCI2000 is a general-purpose system for BCI

BCI2000 supports different kinds of
• Signal acquisition devices
• Signal processing
• BCI applications


BCI2000, filtering module I


ClassifierConnect


Hardware Implementation

• Porti7, with 32 channels, as USB Amplifier.
• LED Array.
• LED Controller, with PIC 16F877.

Experiment:
EEG Cap Configuration


Experiment:
Subject Information


Experiment:
Subject Participation


Condro2010 thesis slide_v3

Recommended

Recommended

More Related Content

Similar to Condro2010 thesis slide_v3

Similar to Condro2010 thesis slide_v3 (20)

Recently uploaded

Recently uploaded (20)

Condro2010 thesis slide_v3