Analyze EEG and MEG data with filtering, epoching, and source localization.
npx skills add https://github.com/tondevrel/scientific-agent-skills --skill mne
Please help me install this Agent Skill. Skill: mne Source: https://github.com/tondevrel/scientific-agent-skills/tree/main/skills/mne Command: npx skills add https://github.com/tondevrel/scientific-agent-skills --skill mne
This Skill streamlines the complex process of analyzing human neurophysiological data, such as EEG and MEG, enabling researchers to gain deeper insights into brain activity.
Use the mne skill to load the sample audvis fif file, filter it between 1 and 40 Hz, and plot the evoked potential.
High-intent search queries and answers about installing and using this skill.
To analyze EEG and MEG data for source localization, you can use this Skill to reconstruct and visualize brain activity in source space. It requires MNE-Python to process complex brain signal recordings and identify neural origins.
The best way to preprocess raw neurophysiological recordings is by applying advanced filtering and artifact removal techniques. This Skill streamlines data preprocessing for EEG, MEG, sEEG, and ECoG formats to prepare clean signals for analysis.
Yes, you can perform epoching, averaging, and frequency analysis on brain signal recordings. This Skill facilitates signal analysis to identify event-related potentials and specific brain response patterns from cognitive experiments.
Yes, MNE-Python supports filtering sEEG and ECoG data between specific frequency ranges. You can quickly load FIF files, apply filters such as 1 to 40 Hz, and plot evoked potentials to interpret complex brain signal recordings.
Yes, you need MNE-Python installed to visualize event-related potentials from cognitive experiments. This Skill requires MNE-Python to process the recordings, identify brain responses, and map their spatial distribution.
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