Guides

Although FinnPy provides numerous tools for the analysis of electrophysiological data, the quality of the results derived using these tools equally depends on correct application and configuration of the former. This section contains guides tackling specific topics from the field of electrophysiological data analysis, providing guide to users of this and other frameworks.

Data preparation

Discussion on General concepts of data preparation prior to feature extraction.

Feature generation

Same frequency coupling may be evaluated using directional absolute coherence, one metric of many provided in FiNNpy. To provide robustness against volume conductance, yet highly sensitive measurements, it is recommended to adjust the metric to the specific use-case. To assist in this effort, FiNNpy provides the DAC Configurator.

Source reconstruction

Explains how Model Computation & Application in the context of M/EEG data.

Statistics

General overview on important statistic concepts relevant for any type of statistical analysis.

Explains how to use Linear Mixed Models supported via FiNNpy to model and evaluate data relationships.

Visualization

To support visualizing the results of cortical analyses, FiNNpy provides functionality to produce Within Python Cortical Plots and for more complex visualizations the option to generate In-Blender Cortical Plots.

Additionally, FiNNpy may be used to visualize Volumetric Data, either within Python or within Blender. As structures from (sub-)cortical atlases may be imported, this provides an attractive opportunity to generate high quality visualizations for subcortical data.