Abstract
The aim of the present study was to evaluate the usefulness of the Random Forest (RF) machine learning technique for identifying most significant frequency components in electroencephalogram (EEG) recordings in order to operate a brain computer interface (BCI). EEG recorded from ten able-bodied individuals during sustained left hand, right hand and feet motor imagery was analyzed offline and BCI simulations were computed. The results show that RF, within seconds, identified oscillatory components that allowed generating robust and stable BCI control signals. Hence, RF is a useful tool for interactive machine learning and data mining in the context of BCI.
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Steyrl, D., Scherer, R., Müller-Putz, G.R. (2013). Random Forests for Feature Selection in Non-invasive Brain-Computer Interfacing. In: Holzinger, A., Pasi, G. (eds) Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. HCI-KDD 2013. Lecture Notes in Computer Science, vol 7947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39146-0_19
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DOI: https://doi.org/10.1007/978-3-642-39146-0_19
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