Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.
Journal:
Biomedizinische Technik. Biomedical engineering
Published Date:
Feb 1, 2016
Abstract
There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.
Authors
Keywords
Adult
Algorithms
Brain-Computer Interfaces
Computer Simulation
Discriminant Analysis
Evoked Potentials, Motor
Evoked Potentials, Somatosensory
Female
Humans
Imagination
Machine Learning
Male
Nonlinear Dynamics
Oscillometry
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Sensorimotor Cortex
Young Adult