Authors:
Patrick Schemrbi
;
Mariusz Pelc
and
Jixin Ma
Affiliation:
Department of Computer and Information Systems, University of Greenwich, Greenwich, London and U.K.
Keyword(s):
Brain-Computer Interface (BCI), Electroencephalography (EEG), Event-Related Potential (ERP), P300 Speller, Distractions, Taxonomy.
Abstract:
In this paper, we investigate the effect that an auditory distraction with differing levels of intensity has on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. This work is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with ten healthy subjects; while using a non-invasive Brain-Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the no music (M0), followed by music at 90% (M90), music at 60% (M60) and last music at 30% (M30), which results were in identical order to the subjects' preferences. In addition, the amplitude did not show any statistical significance in all scenarios while the latency exhibited a minor statistical difference. This work is part of a larger EEG based project where we are introducing different categories of distractions that are being considered alongside
the development of a taxonomy. These results should give some insight into the practicability of the current P300 speller to be used for real-world applications.
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