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Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition.

Nicolás Nieto1,2, Victoria Peterson3, Hugo Leonardo Rufiner4,5

  • 1Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina. nnieto@sinc.unl.edu.ar.

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Summary

This study introduces a new dataset for inner speech recognition using electroencephalography (EEG). This open-access brain-computer interface data will advance AI for thought-controlled devices.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Surface electroencephalography (EEG) noninvasively measures brain activity.
  • Artificial intelligence (AI) has improved Brain-Computer Interfaces (BCIs).
  • Interpreting the 'inner voice' or inner speech phenomenon is a growing research area.

Purpose of the Study:

  • To address the lack of publicly available EEG datasets for inner speech recognition.
  • To provide an open-access, multiclass EEG database of inner speech commands.
  • To facilitate the development of new techniques for inner speech recognition and understanding brain mechanisms.

Main Methods:

  • Acquisition of a ten-participant dataset using a 136-channel EEG system.
  • Recording data across inner speech and two related paradigms.
  • Development of an open-access database for research purposes.

Main Results:

  • A novel, publicly available EEG dataset specifically for inner speech commands has been created.
  • The dataset comprises recordings from ten participants across multiple related paradigms.
  • This resource supports the advancement of AI-driven BCIs for thought-based control.

Conclusions:

  • The presented EEG dataset is a valuable resource for the scientific community.
  • It will aid in the development of more accurate and accessible inner speech recognition systems.
  • This contributes to a better understanding of the neural mechanisms underlying inner speech and its application in BCIs.