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Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but are computationally intensive and can have limited decoding performance. On the other hand, full epoch decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However, they lack methods for interpreting the contributions of spatial and temporal features. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. We demonstrate the approach on 3 different task MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal. Finally, we show that our multiclass model can also be used for pairwise decoding, eliminating the computational burden of training separate models for each pairwise combination of stimuli.