Abstract: The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time.

Mohammad H. Alomari, Ayman AbuBaker, Aiman Turani, Ali M. Baniyounes and Adnan Manasreh, “EEG Mouse:A Machine Learning-Based Brain Computer Interface” International Journal of Advanced Computer Science and Applications(IJACSA), 5(4), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050428

@article{Alomari2014,
title = {EEG Mouse:A Machine Learning-Based Brain Computer Interface},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050428},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050428},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {4},
author = {Mohammad H. Alomari and Ayman AbuBaker and Aiman Turani and Ali M. Baniyounes and Adnan Manasreh}
}