Selección de Canales en Sistemas BCI basados en Potenciales P300 mediante Inteligencia de Enjambre

Martínez-Cagigal, Víctor; Hornero, Roberto

doi:10.1016/j.riai.2017.07.003

Cited by 3 publications

(1 citation statement)

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“…The signal processing pipeline that has been followed in this study is the most common one in the P300-based BCI literature, which applies: down-sampling to 20 Hz as feature extraction, step-wise (SW) regression (max. of 60 features, p in = 0.1, p out = 0.15) as feature selection, and linear discriminant analysis (LDA) as feature classification [4,[15][16][17][18]. As a result, the likelihood of selecting each matrix command is returned, and the final selected command will be the one that provides the maximum probability (i.e., p sel = max p).…”

Section: Processingmentioning

confidence: 99%

Controlling a Smartphone with Brain-Computer Interfaces: A Preliminary Study

Martínez-Cagigal

Santamaría-Vázquez

Hornero

2018

Articulated Motion and Deformable Objects

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Abstract. Nowadays, smartphones are essential parts of our lives. The wide range of functionalities that they offer to us, from calling, taking photos, sharing information or contacting with people, have contributed to make them a useful tool. However, its accessibility remains restricted to disabled people that are unable to control their motor functions. In this preliminary study, we have developed a Brain-Computer Interface system that allows users to control two main functionalities of our smartphones using their own brain signals. In particular, due to the importance of the socializing apps in today's world, the system includes the control of social networking and instant message services: Twitter and Telegram, respectively. The system has been tested with 10 healthy subjects, who were asked to perform several tasks, reaching an average accuracy of 92.3%. Preliminary results show that users can successfully control the system, bridging the accessibility gap in smartphone applications.

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