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real-time-deap-emotion-recognition

https://github.com/mozartuss/real-time-deap-emotion-recognition

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 3 DOI reference(s) in README
  • ○
    Academic publication links
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (4.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: Mozartuss
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 30.3 KB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created about 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks onRaspberry Pi

This is the repository for the paper "A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks onRaspberry Pi" which appeared in HCII 2023, Part II, LNCS 14012 with DOI: 10.1007/978-3-031-35599-8_15.

If this repository was used for your work, we appreciate a citation of our paper.

NOTE

To Use the scripts, please import the DEAP dataset as .dat files into the Data/RAW_DEAP_DATASET

Owner

  • Name: Lukas Kleybolte
  • Login: Mozartuss
  • Kind: user
  • Location: Augsburg
  • Company: University of Applied Sciences Augsburg

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use parts of the repository, please cite it in your paper as below."
authors:
- family-names: "Kleybolte"
  given-names: "Lukas"
  orcid: "https://orcid.org/0000-0002-9886-5592"
- family-names: "Märtin"
  given-names: "Christian"
  orcid: "https://orcid.org/0000-0002-3646-1920"
title: "A Novel EEG-Based Real-Time Emotion Recognition Approach Using Deep Neural Networks on Raspberry Pi"
doi: "10.1007/978-3-031-35599-8_15"
version: 1.0.0
journal: "Human-Computer Interaction. Technological Innovation"
volume-title: "Lecture notes in computer science"
volume: "14012"
publisher: "Springer International Publishing"
editor: "Kurosu, Masaaki and Hashizume, Ayako"
isbn: "978-3-031-35598-1"
month: 7
start: 231 # First page number
end: 244 # Last page number
title: "A Swarm Intelligence Approach: Combination of Different EEG-Channel Optimization Techniques to Enhance Emotion Recognition"
year: 2023

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