A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs

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 f...

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Bibliographic Details
Published in:Journal of neural engineering Vol. 13; no. 6; p. 066015
Main Authors: Breitwieser, Christian, Pokorny, Christoph, Müller-Putz, Gernot R
Format: Journal Article
Language:English
Published: England 01.12.2016
Subjects:
Adult
Algorithms
Brain-Computer Interfaces - classification
Cues
Electroencephalography
Event-Related Potentials, P300 - physiology
Evoked Potentials, Somatosensory - physiology
Female
Fingers - innervation
Fingers - physiology
Humans
Male
Physical Stimulation
Touch - physiology
Young Adult
ISSN:1741-2552, 1741-2552
Online Access:Get more information
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Summary: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. 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. 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. 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.
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ISSN:1741-2552
1741-2552
DOI:10.1088/1741-2560/13/6/066015