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ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.

Donia Metwalli1, Antony E Kiroles2, Yousef A Radwan3

  • 1Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt. d.khaled@nu.edu.eg.

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This study introduces a new Arabic Inner Speech dataset using only eight electrodes, making Brain-Computer Interface (BCI) technology more accessible. The dataset supports five commands, advancing neurotechnology for Arabic speakers.

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Brain-Computer Interface (BCI) technology is increasingly focusing on inner speech over motor imagery for intuitive device control.
  • Existing BCI datasets often require numerous electrodes, hindering the development of cost-effective and accessible systems.
  • A scarcity of publicly available datasets limits research and development in this field.

Purpose of the Study:

  • To introduce a novel, open-access Arabic Inner Speech dataset for Electroencephalographic (EEG) research.
  • To provide a multi-class dataset (five commands) recorded with a minimal number of electrodes (eight) for economical BCI development.
  • To facilitate BCI integration in Arabic-speaking regions and advance neurotechnology.

Main Methods:

  • Development of a new Arabic Inner Speech dataset.
  • Recording EEG data using eight electrodes.
  • Classification of five distinct inner speech commands.

Main Results:

  • A new, cost-effective, multi-class Arabic Inner Speech EEG dataset was created.
  • The dataset utilizes only eight electrodes, offering an economical approach to BCI development.
  • The dataset includes five distinct classes, exceeding the typical number in existing datasets.

Conclusions:

  • The developed dataset addresses the need for accessible and economical BCI resources.
  • This contribution supports the advancement of neurotechnology and BCI applications in Arabic-speaking communities.
  • The open-access dataset will foster further research and innovation in language-specific BCI development.