Brain Computer Interfaces

A Brain Computer Interface (BCI) is a system that provides direct communication pathways between the brain and an external device. These interfaces are a communication alternative, particularly interesting for people with severe motor disabilities. A BCI can be based on different physiological phenomena and it is highly dependent on brain activity signals, classification and feature extraction methods used to predict the user’s intention. The use of this technology in concrete real-world applications still presents several challenges that have hindered its effective implementation. In this line of research, the most recent paradigms are explored and new algorithms are developed to improve the performance and usability of BCIs.

Team members

Nicolás Nieto
Iván Gareis
Leonardo Rufiner

Selected publications

“Thinking out loud”: an open-access EEG-based BCI dataset for inner speech recognition

N. Nieto V. Peterson H. L. Rufiner J. Kamienkoski R. Spies

bioRxiv – 2021

A comparison of feature extraction strategies using Wavelet dictionaries and feature selection methods for single trial P300-based BCI

R. C. Acevedo, Y. Atum, I. E. Gareis, J. Biurrun, V. Medina Bañuelos, H. L. Rufiner

Medical and Biological Engineering and Computing – 2018

OpenBCI & OpenVIBE for P300 speller paradigm: a quick starting guide.

V. Peterson

Technical Report , Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i) (UNL-CONICET), Instituto de Matemática Aplicada del Litoral, IMAL (UNL-CONICET) – 2017

L1-Norm Regularization for Sparse Representation and P300 Wave Detection in Brain-Computer Interfaces

V. Peterson, H. L. Rufiner, R. Spies

V Congreso de Matemática Aplicada, Computacional e Industrial – may 2015

Genetic Feature Selection for a P300 Brain Computer Interface

Y. Atum, J. Biurrun, H. L. Rufiner, R. C. Acevedo

Anales del VI Congreso Latinoamericano de Ingeniería Biomédica (CLAIB 2014), page 165 – oct 2014

On the use of LDA performance as a metric of feature extraction methods for a P300 BCI classification task

I. E. Gareis, Y. Atum, G. Gentiletti, R. C. Acevedo, V. Medina Bañuelos, H. L. Rufiner

Memorias del XVIII Congreso Argentino de Bioingeniería (SABI 2011) – set 2011

Genetic Feature Selection to Optimally Detect P300 in Brain Computer Interfaces

Y. Atum, I. E. Gareis, G. Gentiletti, R. C. Acevedo, H. L. Rufiner

Proc. of the 32nd Annual International IEEE EMBS Conference, page 1711 – 2010