Abstract: In this paper, we address a method for motor imagery feature extraction for brain computer interface (BCI). The wavelet coefficients were used to extract the features from the motor imagery EEG and the linear discriminant analysis was utilized to classify the pattern of left or right hand imagery movement and rest. The performance of the proposed method was evaluated using EEG data recorded by us, with 8 g.tec active electrodes by means of g.MOBIlab+ module. The maximum accuracy of classification is 91%.

Roxana Aldea and Monica Fira, “Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 3(7), 2014. http://dx.doi.org/10.14569/IJARAI.2014.030702

@article{Aldea2014,
title = {Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2014.030702},
url = {http://dx.doi.org/10.14569/IJARAI.2014.030702},
year = {2014},
publisher = {The Science and Information Organization},
volume = {3},
number = {7},
author = {Roxana Aldea and Monica Fira}
}