Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics,
bioengineering and neurophysiology have reduced the gap between human and
machine intelligence. Many methods and algorithms have been developed for analysis
and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution.
The integration of signal processing with the electronic devices serves as a major root for
the development of various biomedical applications. There are many ongoing research
in this area to constantly improvise and build an efficient human- robotic system.
Electroencephalography (EEG) technology is an efficient way of recording electrical activity
of the brain. The advancement of EEG technology in biomedical application helps
in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy
and other malfunctions in human brain.
The main objective of our thesis deals with acquiring and pre-processing of real time
EEG signals using a single dry electrode placed on the forehead. The raw EEG signals
are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored
in the computer. Various machine learning techniques are preferred to classify EEG
signals precisely. Different algorithms are built for analysing various signal processing
techniques to process the signals. These results can be further used for the development
of better Brain-computer interface systems.
2014. , 53 p.
Electrical Engineering with specialisation in Signal Processing & Wave Propagation, Master Programme, 120 credits