Electroencephalograms (EEG) and magnetoencephalography (MEG) are techniques for measuring, directly or indirectly, actual or relative changes in voltage throughout the body. This type of time series data is often related to classification problems. EEG and MEG are also central to human-computer interface research. The question we wish to address on this project is, what is the best way to make predictions from ECG/EEG and MEG? We will treat the problem as time series classification. We have an existing database of publicly available labeled EEG/MEG datasets, tools in both Python for time series classification and collaborators who are experts in traditional techniques for analyzing EEG/MEG. Our ultimate goal is an effective automated end-to-end pipeline for EEG/MEG classification using open-source software such as sktime. This would encourage reproducible research in the field and facilitate more widespread analysis.