About this project
Description from the program archive
Transforming electrophysiology signals from sensors into their frequency representations has been shown to be a way to increase the power for application of this data; for one example of very many see Swann et al., 2015. This Fourier-transformed sensor data can be used to infer data within the brain in a very similar way as the time-series data but with an added, fifth dimension for frequency. MNE-Python has all the components necessary for time-frequency source estimate viewing and would be greatly improved by integrating them to allow for exploration of this complex data. A graphical user interface (GUI) that allows users to look at regions of high power spectral density, while letting the user dynamically adjust the range of frequencies, would be incredibly helpful for allowing this kind of data to be explored and analyzed. The ability to browse slices has already been implemented in `mne.gui.ieeg_locate` and could be abstracted to be used in this case. Time-frequency plotting of spectrograms, with appropriate units and baseline correction already exists in MNE-Python as well. Putting these elements together into a GUI would be a great improvement for the MNE-Python tool and community.