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Epilepsy is a neurological disorder classified as the second most seriousneurological disease known to humanity, after stroke. Localization of theepileptogenic zone is an important step for epileptic patient treatment, whichstarts with epileptic spike detection. The common practice for spike detectionof brain signals is via visual scanning of the recordings, which is asubjective and a very time-consuming task. Motivated by that, this paperfocuses on using machine learning for automatic detection of epileptic spikesin magnetoencephalography (MEG) signals. First, we used the Position WeightMatrix (PWM) method combined with a uniform quantizer to generate usefulfeatures. Second, the extracted features are classified using a Support VectorMachine (SVM) for the purpose of epileptic spikes detection. The proposedtechnique shows great potential in improving the spike detection accuracy andreducing the feature vector size. Specifically, the proposed technique achievedaverage accuracy up to 98\% in using 5-folds cross-validation applied to abalanced dataset of 3104 samples. These samples are extracted from 16 subjectswhere eight are healthy and eight are epileptic subjects using a sliding frameof size of 100 samples-points with a step-size of 2 sample-points

QuPWM: Feature Extraction Method for MEG Epileptic Spike Detection