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bci

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software/bci-enriquetomasmb

Framework for P300 wave detection and noise-based cyberattacks in Brain-Computer Interfaces - Enrique Tomás Martínez Beltrán

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

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
bci · brain-computer-interface · cyberattack · eeg · eeg-analysis · eeg-classification · erp · erp-framework
Regulatory
unknown
built by · 1

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

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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    CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification

  • online_bci_mibrain-computer-interface · eeg · eeg-analysis · eeg-classification

    Real-Time BCI for Rock-Paper-Scissors: Decoding Motor Imagery with Minimal Training

  • EEG-pylineeeg · eeg-analysis · erp

    EEG-pyline is a pipeline for EEG data pre-processing, analysis and visualisation created for neuroscience and mental health research.

  • entropyeeg · eeg-analysis · eeg-classification

    EntroPy: complexity of time-series in Python (DEPRECATED)

  • Muse-Analysis-Toolseeg · eeg-analysis · eeg-classification

    A set of tools to analyze and create charts from Muse EEG devices.

  • aawedhabrain-computer-interface · eeg · erp

    Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking

sources
  1. api.github.com/repos/enriquetomasmb/bci
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2021-11-10, 6 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/34.json→ .entries["bci-enriquetomasmb"]

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