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Peer-reviewedCC BY-NC-ND 4.0Scientific Data

A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals

  • Mario Lobo-Alonso1
  • Iván Martín-Fernández1,2
  • Ignacio Oropesa1,3,4,5
  • Roberto Barra-Chicote6
  • George Kontaxakis1,2
  • Rubén San-Segundo1,2
6 affiliations
  1. 1Universidad Politécnica de Madrid (UPM), E.T.S.I. de Telecomunicación, Madrid, Spain
  2. 2Universidad Politécnica de Madrid (UPM), Information Processing and Telecommunications Center (IPTC), E.T.S.I de Telecomunicación, Madrid, Spain
  3. 3Universidad Politécnica de Madrid (UPM), Biomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical Technology (CTB), Madrid, Spain
  4. 4Instituto de Investigación Hospital 12 de Octubre (imas12), Madrid, España
  5. 5Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
  6. 6Amazon (United Kingdom), Amazon Cambridge Development Centre, Artificial General Intelligence (AGI), Cambridge, UK
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Abstract

This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during Overt Speech (OS) and Covert Speech (CS) in different languages. The dataset comprises repetitions of five different commands pronounced covertly and overtly in English and Spanish from 21 healthy native Spanish speakers (13 male, 8 female, 23 ± 2 years old), while EEG and audio were recorded. In addition, 3 non-native healthy Spanish speakers were recorded under the same circumstances (3 male, 23.3 ± 0.6 years old). A total of 7936 available epochs (i.e., 11.02 hours of data) were recorded with a 32 channel, 256 Hz sampling rate Water based EEG device. The database was designed to maximize the number of different analysis involving EEG signals. The final number of epochs, as well as the statistical analysis (showing significance in Broca’s and Wernicke’s areas) and machine learning experiments (with subjects exceeding the chance level with basic machine learning models), show that this material is a valuable resource for research on future ways of communication.

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Cite

BibTeXRIS
BibTeX
@article{loboalonso2026dataset,
  title = {{A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals}},
  author = {Mario Lobo-Alonso and Iván Martín-Fernández and Ignacio Oropesa and Roberto Barra-Chicote and George Kontaxakis and Rubén San-Segundo},
  journal = {Scientific Data},
  year = {2026},
  month = jul,
  doi = {10.1038/s41597-026-07745-8},
  url = {https://doi.org/10.1038/s41597-026-07745-8},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals
AU  - Mario Lobo-Alonso
AU  - Iván Martín-Fernández
AU  - Ignacio Oropesa
AU  - Roberto Barra-Chicote
AU  - George Kontaxakis
AU  - Rubén San-Segundo
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Data
DO  - 10.1038/s41597-026-07745-8
UR  - https://doi.org/10.1038/s41597-026-07745-8
AB  - This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during Overt Speech (OS) and Covert Speech (CS) in different languages. The dataset comprises repetitions of five different commands pronounced covertly and overtly in English and Spanish from 21 healthy native Spanish speakers (13 male, 8 female, 23 ± 2 years old), while EEG and audio were recorded. In addition, 3 non-native healthy Spanish speakers were recorded under the same circumstances (3 male, 23.3 ± 0.6 years old). A total of 7936 available epochs (i.e., 11.02 hours of data) were recorded with a 32 channel, 256 Hz sampling rate Water based EEG device. The database was designed to maximize the number of different analysis involving EEG signals. The final number of epochs, as well as the statistical analysis (showing significance in Broca’s and Wernicke’s areas) and machine learning experiments (with subjects exceeding the chance level with basic machine learning models), show that this material is a valuable resource for research on future ways of communication.
SN  - 2052-4463
ER  -