Multivariate Pattern Analysis (MVPA), or ’brain decoding’ methods, have become a popular approach for analyzing time series brain recordings such as magnetoencephalography (MEG) and electroencephalography (EEG) to address experimental questions in cognitive neuroscience. Currently, the most common approach is to epoch data relative to an event of interest (e.g., stimulus onset) and apply the classification analyses to relative time points individually. This approach reveals how neural information unfolds over time but neglects any information that may be present in the temporal dynamics of brain activity. In this work, we employ several time series methods (classifiers that incorporate temporal information) with varying time window durations to analyze MEG and EEG data and compare their utility for revealing the temporal dynamics of neural information. As the window duration increases, classifier accuracy improves, but the results become less precise regarding the timing of neural information. We found that a window of \(\sim \) 20 ms resulted in accurate classifiers while maintaining the temporal precision of the results. We also tested time series classifiers based on convolutional kernels, which often outperform simpler methods in benchmark datasets. However, these did not yield better performance in our datasets. Our results suggest that using a brief window, rather than a single time point, could lead to improved sensitivity for MVPA methods to detect neural representations from MEG and/or EEG data.