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

Mario Lobo-Alonso¹, Iván Martín-Fernández^1,2, I Oropesa^1,3,4

¹Universidad Politécnica de Madrid (UPM), E.T.S.I. de Telecomunicación, Madrid, Spain.

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|July 1, 2026

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This study presents the TESSCCo dataset, featuring electroencephalography (EEG) signals from overt and covert speech in English and Spanish. This valuable resource aids research into novel communication methods using brain-computer interfaces.

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Silent speech interfaces are crucial for communication restoration.
Electroencephalography (EEG) offers a non-invasive method for brain activity monitoring.
Existing datasets often lack multilingual covert speech data.

Purpose of the Study:

Introduce the TESSCCo (TV-control EEG-based Silent Speech Command Corpus) dataset.
Provide a comprehensive resource for analyzing EEG signals during overt and covert speech.
Facilitate research in brain-computer interfaces and silent communication technologies.

Main Methods:

Recorded EEG and audio data from 24 healthy native Spanish speakers (21 native, 3 non-native).
Collected data during overt speech (OS) and covert speech (CS) of five commands in English and Spanish.

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Utilized a 32-channel, 256 Hz sampling rate EEG device, resulting in 7936 epochs (11.02 hours).

Main Results:

Statistical analysis revealed significant activity in Broca's and Wernicke's areas.
Machine learning models demonstrated subjects exceeding chance-level performance.
The dataset contains a substantial number of epochs suitable for diverse analyses.

Conclusions:

The TESSCCo dataset is a valuable resource for advancing silent speech communication research.
EEG signals during covert speech contain discriminative information for BCI applications.
This corpus supports the development of future communication systems.