The aim of the present study is to analyze the magnetoencephalogram (MEG) background activity from patients with Alzheimer's disease (AD) and elderly control subjects. MEG recordings from 20 AD patients and 21 controls were analyzed by means of two spectral [median frequency (MF) and spectral entropy (SpecEn)] and two nonlinear parameters [approximate entropy (ApEn) and Lempel-Ziv complexity (LZC)]. In the AD diagnosis, the highest accuracy of 75.6% (80% sensitivity, 71.4% specificity) was obtained with the MF according to a linear discriminant analysis (LDA) with a leave-one-out cross-validation procedure. Moreover, we wanted to assess whether these spectral and nonlinear analyses could provide complementary information to improve the AD diagnosis. After a forward stepwise LDA with a leave-one-out cross-validation procedure, one spectral (MF) and one nonlinear parameter (ApEn) were automatically selected. In this model, an accuracy of 80.5% (80.0% sensitivity, 81.0% specificity) was achieved. We conclude that spectral and nonlinear analyses from spontaneous MEG activity could be complementary methods to help in AD detection.