Open AccessEnsemble Learning in BCI-SSVEP Systems for Short Window Lengths
Author(s) -
Henrique L. V. Giuliani,
Patrick O. de Paula,
Diogo C. Soriano,
Ricardo Suyama,
Denis G. Fantinato
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/ercas.2021.17438
Subject(s) - brain–computer interface , computer science , adaboost , window (computing) , ensemble learning , variance (accounting) , perceptron , artificial intelligence , multilayer perceptron , pattern recognition (psychology) , machine learning , artificial neural network , electroencephalography , classifier (uml) , psychology , accounting , psychiatry , business , operating system
Different approaches have been investigated to implement effective Brain-Computer Interfaces (BCI), translating brain activation patterns into commands to external devices. BCI exploring Steady-State Visually Evoked Potentials usually achieve relatively high accuracy, when considering 2-3 second sample windows, but the performance degrades for smaller windows. So, we investigate the use of an ensemble method, the Adaboost algorithm, combining two different structures, the Logistic Regressor and the Multilayer Perceptron, whose diversity shall bring relevant information for more accurate classification. Simulation results indicate that the proposed method can improve performance for smaller windows, being a promising alternative to reduce model variance.