A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface.
Annu Int Conf IEEE Eng Med Biol Soc
; 2018: 5089-5092, 2018 Jul.
Article
en En
| MEDLINE
| ID: mdl-30441485
ABSTRACT
Motor imagery (MI) based Brain-Computer Interfaces (BCIs) are a viable option for giving locked-in syndrome patients independence and communicability. BCIs comprising expensive medical-grade EEG systems evaluated in carefully-controlled, artificial environments are impractical for take-home use. Previous studies evaluated low-cost systems; however, performance was suboptimal or inconclusive. Here we evaluated a low-cost EEG system, OpenBCI, in a natural environment and leveraged neurofeedback, deep learning, and wider temporal windows to improve performance. $\mu-$rhythm data collected over the sensorimotor cortex from healthy participants performing relaxation and right-handed MI tasks were used to train a multi-layer perceptron binary classifier using deep learning. We showed that our method outperforms previous OpenBCI MI-based BCIs, thereby extending the BCI capabilities of this low-cost device.
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1
Bases de datos:
MEDLINE
Asunto principal:
Electroencefalografía
/
Interfaces Cerebro-Computador
Tipo de estudio:
Health_economic_evaluation
Límite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2018
Tipo del documento:
Article