ContextLab / ContextLab/supereeg
predict best locations
- Dominant language
- Python
- Stars
- 38
- Forks
- 19
- PR merge metrics
- No merged PRs in 30d
Description
Use resting state matrix to create synthetic data, parcellate by region (like Bassett paper), and predict values based on many random samples of n electrodes. Average predicted correlations in regions and across samples to generate a 'best electrode configuration' map.
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by locating the resting-state matrix, synthetic-data generation, regional parcellation, and electrode-prediction entry points. Clarify the expected inputs, number of sampled electrodes, regional correlation aggregation, and output format; done means producing and validating a best-electrode-configuration map.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100