Using electrophysiological measurements, we investigate classification and symptom estimation of individual patients suffering from neurological disorders. Classification algorithms are mainly suited for yes/no problems and are therefore useful for diagnosis and prognosis. Regression algorithms generate a continuous value, which is useful for estimating symptom severity, like fatigue and cognition. The data are EEG and MEG measurements, which can be analysed at sensor level, extracting features from the electrode signals or correlating these signals with each other. A 3D model can be generated from the 2D EEG/MEG data with source reconstruction, which allows to further analyse the data at source level.