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Brain-computer interface (BCI)-generated speech to control domotic devices
- Source :
- RIUMA. Repositorio Institucional de la Universidad de Málaga, instname
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- A brain-computer interface (BCI) is a type of technology that establishes a communication channel between a user and certain devices in the environment via the brain signals of the user. The UMA-BCI Speller tool allows for easy configuration of a BCI, permitting it to be manipulated without the need for much technical knowledge. However, adapting a BCI system so that it can communicate with devices is a challenging task. A simpler technology that is increasingly used to enable communication with devices in the environment is based on voice commands. The aim of the present work is therefore to create a system to facilitate communication between a BCI and devices in the environment using voice commands. Twelve healthy participants and three amyotrophic lateral sclerosis (ALS) patients were asked to control a BCI home automation system. The devices to be controlled were a television, an air conditioner, a smart light bulb, a smart plug, and the WhatsApp and Spotify apps. Performance measures were recorded, and subjective measures were collected based on the System usability scale, NASA-TLX and ad hoc questionnaires. The results of this study validate the proposed system as a suitable option to facilitate communication between a BCI and commercial devices that have been previously adapted to operate based on voice commands. This work is part of the SICCAU project (RTI2018-100912-BI00), which has been jointly funded by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Spanish State Investigation Agency (AEI) and the European Regional Development Fund (ERDF). Moreover, the authors would like to thank all participants for their cooperation. Funding for open access charge: Universidad de Málaga / CBUA.
Details
- ISSN :
- 09252312
- Volume :
- 509
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....3dbc6ef9158412b21917f3cc7f3536c0
- Full Text :
- https://doi.org/10.1016/j.neucom.2022.08.068