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T-BEAR

imported

software/t-bear

Detect EEG artifacts, outliers, or anomalies using supervised machine learning.

Machine-generated from the listed sources and not yet reviewed by a human.

T-BEAR project image
GitHub preview card for peterwhycs/T-BEAR. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Repository
github.com/peterwhycs/T-BEAR
Documentation
unknown
Tags
anomaly-detection · artifact-rejection · artifact-removal · eeg · eeg-classification · eeg-signals-processing · machine-learning · outlier-ensembles · outlier-rejection · outlier-removal · python · python3
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

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    sharing code and data for artifact removal in EEG

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    SigClean is a comprehensive Python library for cleaning and preprocessing biomedical signals including ECG, EMG, EEG, and other physiological signals. It provides a complete toolkit for signal…

  • eeg_mi_dleeg · eeg-classification · eeg-signals-processing

    A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning…

  • Eegle.jleeg · eeg-classification · eeg-signals-processing

    A Julia integrative package for EEG data analysis and machine learning

  • electroCUDAeeg · eeg-classification · eeg-signals-processing

    Robust electrophysiology tools with GPU acceleration

sources
  1. api.github.com/repos/peterwhycs/T-BEAR
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2020-09-19, 13 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/0.json→ .entries["t-bear"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.