Embedded Brain Machine Interface based on motor imagery paradigm to control prosthetic hand

Kevin Acuna, Erick Carranza Urquizo, David Achanccaray

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

10 Citas (Scopus)

Resumen

Brain Machine Interfaces (BMI) have been developed as an alternative way to decode brain signals into control commands and communication devices. A typical BMI uses a computer to process EEG signals; however, current embedded PCs have enough computational resources for fully embedded BMI systems. In this work, the performance of the Odroid-xu4 embedded PC is evaluated as a processing and control device for BMI based on a 2-class motor imagery paradigm. Results show the best accuracy (82.1%) using SVM classifier and minimal processing times (0.11s) on the embedded device, which allows the development of a portable, low cost and trustworthy system.
Idioma originalEspañol
Título de la publicación alojadaProceedings of the 2016 IEEE ANDESCON, ANDESCON 2016
EstadoPublicada - 27 ene. 2017

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