A P300-based brain computer interface for smart home interaction through an ANFIS ensemble

David Achanccaray, Christian Flores, Christian Fonseca, Javier Andreu-Perez

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

26 Citas (Scopus)


Adaptive neuro fuzzy Inference systems (ANFIS) has been applied in brain computer interfaces (BcI) in different ways such as mapping of P300 or fusing information from EEG channels and it has reached high classification accuracy. This work proposes a combination of ANFIS classifiers by voting for a single-trial detection of a P300 wave in a BCI, using four channels; five healthy subjects and three post-stroke patients have participated in this study, each participant performs 4 BCI sessions, crossvalidation is applied to evaluate the classifier performance. The results of average accuracy were greater than 75% for all subjects, similar results were gotten for healthy subjects and post-stroke patients, but the better classifiers for each subject have achieved accuracies greater than 80%.
Idioma originalEspañol
Título de la publicación alojadaIEEE International Conference on Fuzzy Systems
EstadoPublicada - 23 ago. 2017

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