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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Mario Lobo-Alonso,
  • Iván Martín-Fernández,
  • I. Oropesa,
  • Roberto Barra-Chicote,
  • George Kontaxakis,
  • Rubén San-Segundo

摘要

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.