@conference {7323036,
	title = {Classification of motor imagery tasks with LS-SVM in EEG-based self-paced BCI},
	booktitle = {2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC)},
	year = {2015},
	note = {<p>n/a</p>
},
	month = {Oct},
	pages = {244-249},
	abstract = {<p>Motivated by the need to deal with critical disorders that involve death of neurons, such as Amyotrophic Lateral Sclerosis (ALS) and brainstem stroke, interpretation of the brain{\textquoteright}s Motor Imagery (MI) activities is highly needed. Brain signals can be translated into control commands. Electroencephalography (EEG) is considered in this work, EEG is a low-cost non-invasive technique. A big challenge is faced due to the poor signal-to-noise ratio of EEG signals. The dataset used in this work is based on asynchronous or self-paced motor imagery problem. The used self-paced Brain Computer Interface (BCI) problem poses a considerable challenge by introducing an additional class, a relax class, or non-intentional control periods that are not included in the training set and should be classified. In this work, a number of subject dependent parameters and their values are determined. These parameters are: the best frequency range, the best Common Spatial Pattern (CSP) channels, and the number of these CSP channels. System parameters are determined dynamically in the offline training phase. Energy based features are extracted afterwards from the best selected signals. The Least-Squares Support Vector Machine (LS-SVM) classifier is used as a classification back end. Results of the proposed system show superiority over the previously introduced systems in terms of the Mean Square Error (MSE) when tested on the Berlin BCI (BBCI) competition IV dataset 1.</p>
},
	keywords = {band-pass filters, BCI, BCI competition IV dataset 1, brain computer interface, brain-computer interfaces, common spatial pattern, CSP channel, EEG, EEG classification, Electroencephalography, Feature extraction, Information filters, Least squares approximations, least-squares support vector machine, LS-SVM, medical signal processing, MI, motor imagery, motor imagery task classification, self-paced, signal classification, support vector machine, support vector machines, Training},
	doi = {10.1109/ICDIPC.2015.7323036},
	author = {M. E. A. Abdel-Hadi and El-Khoribi, R. A. and Shoman, MI and Refaey, MM}
}
