title: Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.
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.forename: David
authors.initials: D
authors.lastname: Steyrl
authors.forename: Reinhold
authors.initials: R
authors.lastname: Scherer
authors.forename: Josef
authors.initials: J
authors.lastname: Faller
authors.forename: Gernot R
authors.initials: GR
authors.lastname: Müller-Putz
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D016002:Discriminant Analysis; D019054:Evoked Potentials, Motor / Q000502:physiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology; D000069550:Machine Learning; D008297:Male; D017711:Nonlinear Dynamics; D009991:Oscillometry / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D066191:Sensorimotor Cortex / Q000502:physiology*; D055815:Young Adult