REMOVAL OF TMS-INDUCED ARTIFACTS

USING KALMAN FILTER

LIBIN JIJOE E P201331008ME: Medical Electronics

ABSTRACT Transcranial Magnetic Stimulation [TMS] is an effective tool to study brain function.

TMS and EEG used to observe the regional brain activity on cortical stimulation.

Kalman filter approach is used to remove TMS-induced artifacts from EEG recording.

Time-varying covariance matrices suitably tuned on the physical parameters of the problem allows to model the non-stationary components of the EEG/TMS signal.

TRANSCRANIAL MAGNETIC STIMULATION [TMS]

• Coil creates a pulsed magnetic field 20 to 30 ms.

• locally depolarize neurons in brain cortex.

• TMS can be combined with electroencephalography(EEG) to visualize regional brain activity.

WHATS PROBLEM?

SIGNAL AND ARTIFACT

• TMS impulse generates high amplitude and long-lasting artifacts that corrupt the EEG trace

FREQUENCY SPECTRUM OF TMS

EXISTING METHODSON-LINE METHODS

Sample and hold circuit & varying gain amplifier

The second method turns off the amplifiers 10 ms after the impulse.

OFF-LINE METHODS

Wiener filter

KALMAN FILTER

Averages a prediction of a system's state with a new measurement using a weighted average

weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system's state

last "best guess"

Contd…

Square: Matrices Ellipse: Mean and Covariance of Noises Unenclosed values: Vectors• V(n): Stochastic part of noise on TMS.• W(n): White noise.• F: State Transition matrix, B: Input Control matrix

• H: Transformation matrix

SYSTEM EQUATIONS

• State updation

• Covariance updation

• Kalman gain

• Residual covariance

• State prediction

• Covariance prediction

EEG OUTPUT

WEINER vs KALMAN

Fig: A Wiener filter of order 15

Fig: The Kalman filter

REFERENCES• Application of Kalman filter to remove TMS-

induced artifacts from EEG recordings, IEEE Transactions on Control Systems Technology Fabio Morbidi, Andrea Garulli, Domenico Prattichizzo, Cristiano Rizzo and Simone Rossi- Dipartimento di Neuroscience, University of Siena.

• ‘Lecture NOTES’ IEEE Signal Processing- Sept 2012Understanding the basis of the Kalman Filter via a simple intuitive example