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.