A hybrid three-class brain–computer interface system utilizing SSSEPs and transient ERPs. (27th October 2016)

• Record Type: Journal Article
• Authors: Breitwieser, Christian; Pokorny, Christoph; Müller-Putz, Gernot R
• Is Part Of: Journal of neural engineering. Volume 13:Number 6(2016:Dec.)
• Publication Date: 2016-10-27
• Subjects: brain–computer interface -- BCI -- steady-state somatosensory evoked potential -- SSSEP -- P300 -- tERP -- hybrid; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2560/13/6/066015
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain–computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually. Approach. Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA. Main results. A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase. Significance. It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non-control states with the same level of accuracy.