A brain disorder, e.g. epilepsy or Alzheimer’s, is detected by identifying MEG data sets 214 with the brain disorder 220. This is done by training 208 a Support Vector Machine (SVM) by segmenting MEG data from different brain regions (such as left temporal etc.) into possibly one-minute epochs. For each multi-channel segment of a brain region, the signals are concatenated and a set of statistical features is extracted from them, for both healthy brain data and data representing the brain disorder. The extracted statistical features may include maximum or minimum or comprise other parameters such as standard deviation or kurtosis. There may for example be eight statistical features from each of eight brain regions, totalling 64. Following the training phase 202, the trained model 210 is used to classify incoming feature vectors in the diagnosis phase 212, to distinguish e.g. healthy from epileptic data.
