Inner Speech

uploaded by Nicolás Nieto on 2021-04-17 - 2 months ago
last modified on 2021-06-02 - 18 days ago
authored by Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies
18255

OpenNeuro Accession Number: ds003626
Files: 33, Size: 5.78GB, Subjects: 10, Sessions: 3
No Available Tasks
Available Modalities: eeg

README

Inner Speech Dataset.

Author: Nicolas Nieto

Code available at https://github.com/N-Nieto/Inner_Speech_Dataset Prepreint available at https://www.biorxiv.org/content/10.1101/2021.04.19.440473v1

Abstract: Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtain with an acquisition systems of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

Conditions = Inner Speech, Pronounced Speech, Visualized Condition

Classes = "Arriba/Up", "Abajo/Down", "Derecha/Right", "Izquierda/Left"

Total Trials = 5640

Please contact us at this e-mail address if you have any doubts: nnieto@sinc.unl.edu.ar

Authors

  • Nicolas Nieto
  • Victoria Peterson
  • Hugo Rufiner
  • Juan Kamienkowski
  • Ruben Spies

Dataset DOI

10.18112/openneuro.ds003626.v1.0.3

License

CC0

Acknowledgements

How to Acknowledge

Funding

Ethics Approvals

  • Comité Asesor de Ética y Seguridad en el Trabajo Experimental (CEySTE), CCT-CONICET, Santa Fe,

How To Cite

Copy
Nicolas Nieto and Victoria Peterson and Hugo Rufiner and Juan Kamienkowski and Ruben Spies (2021). Inner Speech. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003626.v1.0.3
More citation info

Inner Speech

uploaded by Nicolás Nieto on 2021-04-17 - 2 months ago
last modified on 2021-06-02 - 18 days ago
authored by Nicolas Nieto, Victoria Peterson, Hugo Rufiner, Juan Kamienkowski, Ruben Spies
18255

OpenNeuro Accession Number: ds003626
Files: 33, Size: 5.78GB, Subjects: 10, Sessions: 3
No Available Tasks
Available Modalities: eeg

README

Inner Speech Dataset.

Author: Nicolas Nieto

Code available at https://github.com/N-Nieto/Inner_Speech_Dataset Prepreint available at https://www.biorxiv.org/content/10.1101/2021.04.19.440473v1

Abstract: Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a “natural” way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-subjects dataset acquired under this and two others related paradigms, obtain with an acquisition systems of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.

Conditions = Inner Speech, Pronounced Speech, Visualized Condition

Classes = "Arriba/Up", "Abajo/Down", "Derecha/Right", "Izquierda/Left"

Total Trials = 5640

Please contact us at this e-mail address if you have any doubts: nnieto@sinc.unl.edu.ar

Authors

  • Nicolas Nieto
  • Victoria Peterson
  • Hugo Rufiner
  • Juan Kamienkowski
  • Ruben Spies

Dataset DOI

10.18112/openneuro.ds003626.v1.0.3

License

CC0

Acknowledgements

How to Acknowledge

Funding

Ethics Approvals

  • Comité Asesor de Ética y Seguridad en el Trabajo Experimental (CEySTE), CCT-CONICET, Santa Fe,

How To Cite

Copy
Nicolas Nieto and Victoria Peterson and Hugo Rufiner and Juan Kamienkowski and Ruben Spies (2021). Inner Speech. OpenNeuro. [Dataset] doi: 10.18112/openneuro.ds003626.v1.0.3
More citation info

Dataset File Tree

Git Hash: 28b0081 

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Dataset File Tree

Git Hash: 28b0081 

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