Digital Signal Processing and System Theory

Talk Wiebke Schmidt


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Noise Reduction in EEG and MEG Signals

Date: 25.09.2013, 13:00 h - 14:00 h
Room: Aquarium

Wiebke Schmidt
CAU, Kiel, Germany,




Details



Magnetoencephalography (MEG) and electroencephalography (EEG) denote the measurements of currents within the brain. Currently the most used method for recordings is EEG. Due to the fact that the MEG sensors have to be liquid helium cooled, MEG recordings are very expensive. However, MEG offers some advantages in comparison to EEG. One of the advantages is that the measurement can be done without contacting the scalp. Therefore, the time consumption for the setup of the measurements is considerably smaller than EEG.

With the help of EEG and MEG, many brain disorders and also the area of damage can be identified. That is very important in the Medical Technology area. There are many applications where the brain signals can be used to. For example, electronics such as the television can be controlled only with thoughts.It can also be applicable in cars to avert, for example, an accident,
because the brain knows several milliseconds earlier, that the car should be braked. The sensors could be assembled in the headliner of the car. By a connection of the sensors with the car electronic, the car will brake before the signal with the information to brake arrived the leg.      

There are new magnetoelectric (ME) sensors that are being developed at Kiel University. These sensors are cheaper and they can be operated at room temperature. As a result, MEG examinations can be done with lower costs. However, the main problem is that the measurements present noise from different sources (amplifier, sensor itself, computers etc.) which may hide the brain signals. For that reason, an enhancement has to be done.

The purpose of this Thesis is to reduce the noise from the EEG/MEG signals. To achieve that, the sensor behavior was simulated and an algorithm to estimate the noise and then to enhance the signals were implemented in MATLAB.