  Epilepsy is a neurological disorder classified as the second most serious
neurological disease known to humanity, after stroke. Localization of the
epileptogenic zone is an important step for epileptic patient treatment, which
starts with epileptic spike detection. The common practice for spike detection
of brain signals is via visual scanning of the recordings, which is a
subjective and a very time-consuming task. Motivated by that, this paper
focuses on using machine learning for automatic detection of epileptic spikes
in magnetoencephalography (MEG) signals. First, we used the Position Weight
Matrix (PWM) method combined with a uniform quantizer to generate useful
features. Second, the extracted features are classified using a Support Vector
Machine (SVM) for the purpose of epileptic spikes detection. The proposed
technique shows great potential in improving the spike detection accuracy and
reducing the feature vector size. Specifically, the proposed technique achieved
average accuracy up to 98\% in using 5-folds cross-validation applied to a
balanced dataset of 3104 samples. These samples are extracted from 16 subjects
where eight are healthy and eight are epileptic subjects using a sliding frame
of size of 100 samples-points with a step-size of 2 sample-points
