PyRank
EEGo-UNC

eegproc

EEGProc is a fully vectorized library designed for deep learning using EEG (Electroencephalogram) data. This library is optimized for performance and ease of use, making it suitable for researchers and developers working in the field of neuroscience, biomedical engineering, and machine learning.
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Download Stats Changelog Documentation Issues
Verified repo
Monthly downloads
453
Weekly downloads
405
GitHub stars
5

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
2.0.0
Requires Python
>=3.10
Licence
GNU Lesser General Public License
Last Release
1 week ago
First Release
October 1, 2025
Total Releases
2
Python Versions
3 3.10 3.11 3.12 3.13
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
60
Popularity
13
Adoption
26
Community
40
Provenance
100
How the maintenance status was determined
Last commit
12 days ago
Monthly downloads
453
• Active
Activity within 30 days
Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
453
Stars
5
Stars per 1k downloads
11.04
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
• Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
58
Month daily avg
15
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
More insights and metrics
Python version support tiers
requires_python
>=3.10
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
• Legacy
Supports 3.10 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
GNU Lesser General Public License
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
12 days ago
Total releases
2
Releases/year
1.9
Rapid
Last release within 365 days and > 12 releases/year
• Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
10
Monthly downloads
453
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
18
 
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
1000.0%
contrib / forks
Stale Release
No
 
PR Velocity
Quiet
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

No OS data available

Python Versions

No Python version data available

Top Countries

1. US US
10
2. FR FR
5
3. DE DE
2

Weekly Downloads

GitHub Stars

Dependencies

6
15 optional dependencies

Used By

0
Not used by any tracked packages

Repository details

Forks
1
Issues
11
Last Commit
1 week ago
Contributors
10
Open PRs
0
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
eeg entropy preprocessing featurization neuroscience bci cross-validation
Sources and methodology